22 postdoc, research fellow and PhD student positions in artificial intelligence and machine learning

Are you an ambitious researcher looking for an interesting postdoc, research fellow or PhD position?

Photo: Matti Ahlgren / Aalto University

Photo: Matti Ahlgren / Aalto University

Finnish Center for Artificial Intelligence FCAI offers a possibility for 22 new researchers to join a unique research community with an attractive joint mission.

FCAI is a community of experts that brings together top talents in academia, industry, and public sector to solve real-life problems using both existing and novel AI. FCAI is built on the long track record of pioneering machine learning research in Helsinki with currently over 60 professors from Aalto University and University of Helsinki contributing to our research. Our lively AI research community  organizes frequent seminars with prominent speakers and offers high-quality collaboration opportunities with other leading research networks and companies (e.g. FCAI hosts ELLIS unit Helsinki and has a joint research center with NVIDIA). Local and national computational services spearheaded by the future EuroHPC supercomputer LUMI (200+ Pflops/s) provide our researchers with access to excellent computing facilities.

FCAI’s research mission is to create a new type of AI that is data efficient, trustworthy, and understandable. We aim to build AI systems capable of helping their users in AI-assisted decision-making, design and modeling. We develop AI techniques needed for systems which can help their users make better decisions and design better solutions across a range of tasks from personalized medicine to materials design. A core insight in developing such AIs is that they need to have world models for understanding the world and interacting with it, and user models for understanding the user and interacting with them. This is advanced in seven Research Programs and in Highlights that make sure FCAI’s fundamental AI research is taken into real-life use. Read more about our research here.

We provide an exceptional opportunity for 22 new researchers to join the FCAI community, where a great number of experts from a range of fields work towards the common research mission. The new positions are designed to further strengthen this research from diverse angles and offer a possibility for across-field collaboration through joint supervision by FCAI professors.

Depending on the position, we are looking for mainly postdocs, but also excellent research fellow and PhD student candidates can be considered for some of the positions. The positions are negotiated on an individual basis and may include e.g. relocation bonus and independent travel budget. We welcome applicants with diverse backgrounds, and qualified female candidates are explicitly encouraged to apply.

The deadline for applications is October 5, 2020 (midnight UTC+02:00)

TOPIC 1: ACTIVE LEARNING FOR DECISION-MAKING

While basic active learning is usually applied for acquiring labels of unlabeled samples for supervised learning, the principle is more general. We develop methods for deciding what to measure or what data to collect, for a decision-making task which is initially partially unknown and has to be learned as well. Prime examples are learning of models of decision makers, or reward functions. The methods are central in developing AI-assisted decision-making tools. We provide good opportunities of applying the tools in personalized healthcare and medicine.

  • Supervision: Samuel Kaski (Aalto University), Pekka Marttinen (Aalto University)

  • Keywords: Active learning, decision-making, sequential design of experiments

  • Most relevant FCAI research programs: Agile probabilistic AI; Autonomous AI, Simulator-based inference; Next generation data-efficient deep learning; Interactive AI;  Easy and privacy-preserving modeling tools

  • Level: Postdoctoral researcher or research fellow

TOPIC 2: AI FOR THERAPY OPTIMIZATION

Predictability and control of evolving populations is an emerging topic of high scientific interest and vast translational potential in applications such as vaccine design and cancer therapy [1,2]. This project will develop inference approaches to optimally control dynamical systems (say cell populations) in a (partially) model free setting, the killer application being to derive optimal control (therapy) protocols for minimal models of cancer or microbial cell populations, without prior knowledge of the evolution equations. We assume an access to a simulator that can be used to generate outcomes of the process under some control protocol that is decided by us. Then we need to find an iterative solution that tells how to update the sampling control before generating another set of realisations from the simulator. It will be critical that not many iteration rounds are needed, so Bayesian optimization/ ELFI will be used and developed further [3]. The simulator can be at a first instance computational but can also be thought as a real biological experiment which can be further pursued with experimental collaborators.

  • Supervision: Ville Mustonen (University of Helsinki); Jukka Corander (University of Helsinki)

  • Keywords: Stochastic optimal control, likelihood-free Inference, bayesian inference

  • Most relevant FCAI research programs: Simulator-based inference, AI for applications in healthcare

  • Level: Postdoctoral researcher

[1] Lässig M, Mustonen V, Walczak AM (2017) Predicting evolution. Nat Ecol Evol 1(3):1–9.
[2]  Lässig M, Mustonen V, (2020) Eco-evolutionary control of pathogens. PNAS, (in press).
[3]  Lintusaari J, [6 authors], Vehtari A, Corander J, Kaski S  (2018) ELFI: Engine for likelihood-free inference. J Mach Learn Res 19:1–7.

TOPIC 3: AI-ASSISTED DESIGN OF INTERVENTIONS

The problem of choosing interventions is an exciting combination of causal inference and sequential experimental design, needed for both experimental scientific research and choosing medical treatments, for instance. We additionally take a human expert in the loop, having prior knowledge which can be queried, and whose goal is to learn about the causal effects in the process.

  • Supervision: Samuel Kaski (Aalto University), Kai Puolamäki (University of Helsinki), Antti Oulasvirta (Aalto University)

  • Keywords: Causal inference, sequential experimental design, interactive machine learning

  • Most relevant FCAI research programs: Agile probabilistic AI; Autonomous AI; Easy and privacy-preserving modeling tools; Simulator-based inference; Interactive AI; Next generation data-efficient deep learning

  • Level: Postdoctoral researcher or research fellow

TOPIC 4: AI-ASSISTED MODELLING

Modelling is a combination of a design task of building the model, and the statistical task of fitting the model to data or more generally statistical inference. While probabilistic programming is progressing fast on the latter part, and AutoML helps when there is enough data, much less help exists for the design task. We formulate the task of offering design help as a broader modelling task, which includes the modeller in the loop. The solution of the broader task gives both the AI-assistance and a solution of the primary task.

  • Supervision: Samuel Kaski (Aalto University), Aki Vehtari (Aalto University), Arto Klami (University of Helsinki), Antti Oulasvirta (Aalto University)

  • Keywords: Sequential design of experiments, prior elicitation

  • Most relevant FCAI research programs: Agile probabilistic AI; Interactive AI;  Easy and privacy-preserving modeling tools; Simulator-based inference; Next generation data-efficient deep learning

  • Level: Postdoctoral researcher or research fellow

TOPIC 5: ATMOSPHERIC AI

Artificial intelligence (AI) and machine learning (ML) are making their inroads to atmospheric and earth sciences. There are lots of opportunities to do research in physical sciences more efficiently and to obtain novel results of high impact—both in atmospheric and computer sciences—by developing and applying novel AI methods to solve scientific problems. In this project, we plan to build probabilistic models of measured and simulated natural world phenomena, trained by using simulator outputs or real-world observations, which allow us for example replace computationally expensive simulator runs with faster ML computations, to fill in missing data from observations, and to better understand complex systems and processes and underlying causal relations. Our objective is to also model the interactive data analysis and model building process of the substance area experts (here atmospheric scientists), which allows us to address problems such as as how to design the exploratory data analysis workflows and systems and how to best incorporate the knowledge and insights of the experts into the model building process. We are looking for an atmospheric scientist with interest in AI, or a computer scientist who wants to develop AI methodology and work with physics-related applications. We can adjust the work plan and the supervision arrangement depending on the qualifications and interests of the hired person. The AI part of the project will be executed in collaboration with the relevant FCAI AI research programmes.

  • Supervision: Kai Puolamäki (University of Helsinki); potential co-supervisors Hanna Vehkamäki (University of Helsinki), Leena Järvi (University of Helsinki), Tuomo Nieminen (University of Helsinki)

  • Keywords: Atmospheric and earth sciences; exploratory data analysis; automatic experimental design; interactive user modelling; causal inference

  • Most relevant FCAI research programs: Agile probabilistic AI; Simulator-based inference; Interactive AI; Intelligent urban environment

  • Level: Postdoctoral researcher or PhD student

TOPIC 6: COMPUTATIONAL COGNITIVE MODELS

To reason about human behavior, and to plan actions and interventions, a strong prior is needed. The goal of this project is to develop computational models of basic cognitive capabilities, such as attention, memory, or decision-making. We in particular focus on rational models (e.g., bounded rationality, computational rationality) that can approximate behavioral policies. These models can support interactive AI in many ways, including training of ML models  (e.g., offline reinforcement learning), planning problems (e.g., model-based reinforcement learning), and inference of human data (e.g., using ABC).

  • Supervision: Antti Oulasvirta (Aalto University); other possible supervisors Samuel Kaski (Aalto University), Jukka Corander (University of Helsinki), Ville Kyrki (Aalto University)

  • Keywords: Computational cognitive models, cognitive simulations, computational rationality, interactive AI

  • Most relevant FCAI research programs: Interactive AI; Simulator-based inference; Next generation data-efficient deep learning; Easy and privacy-preserving modeling tools; AI-driven design of materials

  • Level: Postdoctoral researcher

TOPIC 7: DATA-EFFICIENT DEEP LEARNING FOR ROBUST RETINAL DISEASE DETECTION

The goal is to develop and apply data-efficient Deep Learning (DL) methods for robust detection and classification of retinal diseases: diabetic retinopathy, diabetic macular edema, age-related macular degeneration, and glaucoma [1]. The focus is to increase trust in and reliability of DL methods in the decision-critical medical domain and its applicability for small annotated datasets, as well as developing robust DL for improved model calibration in on-going collaboration with the Alan Turing Institute and Helsinki University Hospital (HUS). The data-efficiency is explored by semi- and self-supervised learning and in difficult cases by adding dimensions through additional patient or multimodal data. Our medical collaborators in the Central Finland Central Hospital, Folkhälsan, HUS Eye-clinic & Glaucoma Center share large and unique sets of annotated Retinal, Optical Coherence Tomography (OCT) and other medical images, with auxiliary clinical patient data (such as patient’s blood glucose and disease & treatment & medication history data) but also large quantities of unannotated image data. The utilization of additional data would not only amount for novel studies in the domain of medical image analysis, but when applied, it could benefit the performance of such algorithms and models. The aim is that the deep learning systems are to be designed with uncertainty quantification and auxiliary medical information in mind, which would enable the system to recommend certain diagnoses to the medical expert and also quantify the certainty of recommendation. Due to having access to a number of very large and medically unique datasets of various kind (HUS, Central Finland’s Central  Hospital, Digifundus Ltd, Folkhälsan), one of our additional goals is to develop deep learning methods for generating synthetic differentially private dataset for the purpose of wide-scope AI-methodology research, medical doctor training, and digitalisation of health care.

  • Supervision: Kimmo Kaski (Aalto University), Simo Särkkä (Aalto University), Antti Honkela (University of Helsinki), Pekka Marttinen (Aalto University), Samuel Kaski (Aalto University)

  • Keywords: Deep Learning, robust learning supervised, semi-supervised and self-supervised methods, model calibration, AI-assisted medical decision-making, synthetic differentially private data

  • Most relevant FCAI research programs: Next generation data-efficient deep learning; Applications of AI in healthcare; AI’s for AI-assisted design, decision-making and modelling; Security and privacy

  • Level: Doctoral student or postdoctoral researcher

[1] Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K (2019), Deep Learning Fundus Image Analysis for Diabetic Retinopathy and Macular Edema, Nature Scientific Reports, 9, 10750.

TOPIC 8: DEEP LEARNING IN MATERIALS ANALYSIS

Many challenges in interpretation of materials data are obviously suited to AI-assisted techniques and great progress has been made recently in data-mining with materials’ simulations. A step beyond this, is the introduction of AI tools into the direct analysis of experimental materials’ data, which brings a host of new challenges in the nature of the data and the development of accurate models for training. In this project, we focus on the development of new deep learning tools for the analysis of materials data, particularly 3D imaging data where robust uncertainty predictions are essential. This will be integrated into the Aalto Materials Digitalization (AMAD) platform for the storage, analysis and dissemination of materials data. The candidate is expected to have experience in applying AI methods in scientific data analysis and a PhD in related topics. Direct exposure to materials’ science research would be desirable.

  • Supervision: Adam Foster (Aalto University); Juho Kannala (Aalto University), Alexander Ilin (Aalto University)

  • Keywords: Materials, deep learning, infrastructure development

  • Most relevant FCAI research programs: Next generation data-efficient deep learning; AI-driven design of materials

  • Level: Postdoctoral researcher

TOPIC 9: DEEP LIKELIHOOD-FREE INFERENCE

Simulator-type models are widespread in engineering and sciences. They can generate data but do not have a tractable likelihood, and need so-called likelihood-free inference or approximate Bayesian computation for inferring parameters. Deep learning is showing promise as surrogate models for the inference, but bottlenecks remain in coping with small data sets, and in computation speed. We develop Bayesian deep learning methods for the data-efficiency needed for time-varying or adaptive simulators, and tradeoffs needed for computational speed in particular for interactive applications.

  • Supervision: Samuel Kaski (Aalto University), Jukka Corander (University of Helsinki), Jaakko Lehtinen (Aalto University)

  • Keywords: Bayesian deep learning, likelihood-free inference, implicit models

  • Most relevant FCAI research programs: Simulator-based inference; Next generation data-efficient deep learning; Agile probabilistic AI; Easy and privacy-preserving modeling tools; Interactive AI

  • Level: Postdoctoral researcher or research fellow

TOPIC 10: INTERACTIVE AI FOR PERSONALIZED INTERACTIVE INFORMATION RETRIEVAL AND RECOMMENDER SYSTEMS

Reinforcement learning is widely used in many interactive AI applications in the domain of interactive information retrieval and recommender systems. However, these methods transfer poorly and may require the collection of extensive user data. This project builds on existing research in reinforcement/active learning, transfer learning, user modelling and cognitive modelling. The goal is to enable fast personalization without collecting extensive user data and being transparent to the user.

  • Supervision: Dorota Glowacka (University of Helsinki), Alan Medlar (University of Helsinki); potential co-supervisor Antti Oulasvirta (Aalto University)

  • Keywords: Interactive information retrieval, recommender systems, transfer learning, active learning, reinforcement learning, user modelling, cognitive modelling

  • Most relevant FCAI research programs: Interactive AI; AI in society; Autonomous AI

  • Level: Postdoctoral researcher; outstanding PhD student candidates can also be considered

TOPIC 11: INTERACTIVE AI USING MULTIMODAL COMMUNICATION

Intelligent machines require not only an internal model of the world but also interaction with humans and their environment. They need to make use of contextualized information and must be able to adapt to the user. This project focuses on multimodal communication that is natural for humans but difficult for machines and will deal with spontaneous spoken and written language, gestures and other forms of non-verbal types of interaction. The important aspect is to model those channels in combination to be able to deal with complementary information coming from audio-visual signals as well as traditional types of input coming from keyboards and touch-sensitive devices. We envision the development of a multimodal assistant that can interact with its users in a coherent natural way. The project will include work on cross-modal attention and sequence models that are able to capture long-distance dependencies and we will test our ideas in applications related to health and wellbeing. The framework for this work will be based on modern neural architectures and deep learning and combines aspects of supervised, unsupervised and reinforcement learning. This research problem will be solved as a collaboration between Helsinki-NLP and Aalto ASR, Video Content Analysis and User Interfaces research group, all in FCAI.

  • Supervision: Jörg Tiedemann (University of Helsinki); Mikko Kurimo (Aalto University), Jorma Laaksonen (Aalto University), Antti Oulasvirta (Aalto University)

  • Keywords: Multimodal NLP, speech technology, computer vision

  • Most relevant FCAI research programs: Interactive AI; Next generation data-efficient deep learning; AI in society; Intelligent service assistants / Intelligent urban environment

  • Level: Postdoctoral researcher with relevant background in computer science, computational linguistics or computer vision and experience with practical development of deep learning applications; previous work on multimodal approaches is a plus

TOPIC 12: INTERACTIVE USER MODELS

Most machine learning systems operate with us humans, to augment our skills and assist us in our tasks. In environments containing human users, or, more generally intelligent agents with specific goals and plans, the system can only help them reach those goals if it understands them. Since the goals can be tacit and changing, they need to be inferred from observations and interaction. We develop the probabilistic interactive user models and inference techniques needed to understand other agents and how to assist them more efficiently.

  • Supervision: Samuel Kaski (Aalto University), Ville Kyrki (Aalto University), Antti Oulasvirta (Aalto University), Andrew Howes (University of Birmingham, visiting professor at the Aalto University)

  • Keywords: Active learning, experimental design, knowledge elicitation, multi-agent learning, machine teaching, reinforcement learning, computational rationality

  • Most relevant FCAI research programs: Interactive AI;  Agile probabilistic AI; Simulator-based inference; Next generation data-efficient deep learning; Easy and privacy-preserving modeling tools; AI-driven design of materials

  • Level: Postdoctoral researcher or research fellow

TOPIC 13: MACHINE LEARNING FOR EXPERIMENTAL DESIGN

We bring machine learning techniques to designing the design-build-test-learn cycles which are ubiquitous in engineering systems, and experimental design in sciences and medicine. This needs Bayesian experimental design techniques able to work well with both simulators, measurement data and humans in the loop, who are both information sources and the final decision makers. We are looking for a researcher interested in developing the new methods, with options on applying the techniques to improve modelling in synthetic biology, interface design and medicine.

  • Supervision: Samuel Kaski (Aalto University), Ville Kyrki (Aalto University)

  • Keywords: Sequential design of experiments, Bayesian experimental design, likelihood-free inference, implicit models

  • Most relevant FCAI research programs: Simulator-based inference; Agile probabilistic AI; Interactive AI;  Next generation data-efficient deep learning; Easy and privacy-preserving modeling tools; AI-driven design of materials

  • Level: Postdoctoral researcher or research fellow

TOPIC 14: PRIOR CONSTRAINTS IN PROBABILISTIC PROGRAMMING

Using prior domain knowledge on model parameters and transformations is at the core of Bayesian modeling, helping to build more interpretable models that can be estimated from less data. This project develops richer ways of encoding prior knowledge, focusing on incorporating (soft and hard) constraints into probabilistic programs. The current tools support simple constraints (non-negativity of a parameters, or linearity of a function) but often the prior knowledge is in form of more complex constraints (e.g. monotonicity or near-linearity of a function, or permutation invariance) that remain challenging. Building on existing theoretical foundations for specific cases, you will work on developing both the theory and practical inference algorithms for handling such constraints.

The position is ideal for candidates with strong background in Bayesian modeling or machine learning. Your main task is to develop the required computational methods and ideally proceed to implement them into existing probabilistic programming tools in collaboration with others. We already have several concrete applications with such prior knowledge (e.g. physical knowledge in material design and cognitive theories in decision-making), and you will work in collaboration with other FCAI projects to apply the methods in selected interesting use-cases.

  • Supervision: Arto Klami (University of Helsinki); Aki Vehtari (Aalto University)

  • Keywords: Probabilistic programming, Bayesian modeling, prior knowledge

  • Most relevant FCAI research programs: Agile probabilistic AI; Next generation data-efficient deep learning; Interactive AI; Easy and privacy-preserving modeling tools

  • Level: Postdoctoral researcher; outstanding PhD student candidates can also be considered

TOPIC 15: PROBABILISTIC DEEP LEARNING FOR ELECTRONIC HEALTH RECORDS

We will develop deep neural networks for healthcare time series data. This work is done in collaboration with the national healthcare authorities and the main healthcare providers in the Helsinki region. In particular, we will focus on probabilistic deep learning to capture predictive uncertainty, critical in healthcare applications. We combine these models with techniques for causal inference, to facilitate robust and reliable decision-making on individual and population levels.

  • Supervision: Pekka Marttinen (Aalto University); Alexander Ilin (Aalto University); Harri Lähdesmäki (Aalto University)

  • Keywords: Deep learning, uncertainty, causality, electronic health records

  • Most relevant FCAI research programs: Applications of AI in healthcare; AI in society; Next generation data-efficient deep learning; Agile probabilistic AI

  • Level: Postdoctoral researcher or PhD student

TOPIC 16: SELF-SUPERVISED DEEP LEARNING FOR COMPUTER VISION

AIs need to have world models for understanding the world and interacting with it. These models typically need to be learned from data and integrated into decision-making processes. However, the problem of obtaining suitably annotated training data is a key challenge in many machine learning problems, where deep neural networks are seen as promising models. In certain areas, such as geometric computer vision, one can utilize redundancy in the data and physics of image formation to formulate constraints that allow learning powerful representations from raw data streams (e.g., videos from stereo cameras). In this project, the aim is to develop self-supervised deep learning methods. Possible application areas include perception in terms of odometry, optical flow, and multi-view stereo, and reinforcement learning systems.

  • Supervision: Juho Kannala (Aalto University); Arno Solin (Aalto University)

  • Keywords: Computer vision, machine learning, deep learning

  • Most relevant FCAI research programs: Next generation data-efficient deep learning; Simulator-based inference

  • Level: Postdoctoral researcher

TOPIC 17: VISUALIZATION IN MODELING WORKFLOW

We develop AI techniques needed for systems which can help their users make better decisions and design better solutions across a range of tasks from personalized medicine to materials design. A core insight in developing such AIs is that they need to have world models for understanding the world and interacting with it, and user models for understanding the user and interacting with them. Many parts of the probabilistic modeling workflow benefit from visualization. This project develops tools for AI-assisted visualizations using AI which has a theory of mind of the user. The work will be built on existing theory in cognitive sciences and human-computer interaction. The goal is to generate visually appealing, task-specific, and informative visualizations with controllable complexity depending on the amount of information that is available, required, and sensible given the expertise of the user.

  • Supervision: Aki Vehtari (Aalto University); Antti Oulasvirta (Aalto University)

  • Keywords: Interactive probabilistic modeling, modeling workflow, visualization, uncertainty quantification, decision-making

  • Most relevant FCAI research programs: Agile probabilistic AI; Interactive AI; Easy and privacy-preserving modeling tools

  • Level: Postdoctoral researcher

Topics with a special emphasis on AI across fields collaboration

TOPIC 18: AI AND MEDICAL IMAGING

The aim is to develop AI-based diagnostics and analytics along with designing very low field (VLF) MRI techniques enabling new transformational point-of-need services in both healthcare and wellness. The collaborating parties are Aalto University’s Electrical Engineering and Automation Department (EEA/ELEC) and the Design Department (DoD/ARTS). EEA will use machine learning enhanced image reconstruction methods in developing data acquisition and processing methods for the VLF-MRI data. Machine learning approaches will also be used for removal of imaging artefacts as well as improving data visualization. DoD will take part in the design of user interfaces to translate the underlying artificial reasoning into efficient human-to-machine communication.

The position is ideal for a candidate who is interested in medical applications of AI-methods such as deep learning, GANs, and Bayesian computations. Your task would be to develop the intelligent reconstruction and analysis methods in collaboration with medical imaging and hardware design experts.

  • Supervision: Simo Särkkä (Artificial Intelligence, Aalto University); Ilkka Laakso (Electromagnetics in Health Technologies, Aalto University), Severi Uusitalo (Industrial Design, Aalto University)

  • Keywords: Medical imaging, MRI, deep learning, Bayesian computation

  • Most relevant FCAI research programs: Next generation data-efficient deep learning; Agile probabilistic AI; Interactive AI

  • Level: Postdoctoral researcher

TOPIC 19: AI AND NEUROSCIENCE

The aim would be to use AI-methods to shed light on the neural underpinnings of visually-guided behavior in mice and humans. Vision offers a remarkable possibility for an end-to-end characterization of neural processing of sensory information from photons to behavior. The collaborators are performing state-of-the-art behavioral experiments in mice and humans while simultaneously measuring activity of relevant neural circuits in mice and cortical areas in humans. AI models and methods could be used both in tracking freely-moving animals and in extracting behavior-linked information from the acquired neural data. The work would be based on the combination of the participants’ works [Smeds et al. Neuron, 2019; Misiewicz et al. PLoS Genet., 2019; Zubarev & Parkkonen Neuroimage, 2018; Särkkä et al. TransAutCtrl, 2019].

The position is ideal for a candidate who is interested in AI-methods such as deep neural networks, GANs, Bayesian computations, and Markov decision processes, not only as tools for modeling data, but also as ways to model the behaviour and brain function of animals. Your task would be to build connections between the AI-models and neural circuit models as well as compare their behaviour by simulations and based on data collected from actual mice.

  • Supervision: Simo Särkkä (Artificial Intelligence, Aalto University); Petri Ala-Laurila (Neuroscience and Biomedical Engineering, Aalto University), Iiris Hovatta (Psychology and Logopedics, University of Helsinki), Lauri Parkkonen (Neuroscience and Biomedical Engineering, Aalto University)

  • Keywords: Neural circuits, AI-based models, modeling of animal brain, deep learning, Bayesian computation

  • Most relevant FCAI research programs: Agile probabilistic AI; Next generation data-efficient deep learning

  • Level: Postdoctoral researcher

پیشنهاد مطالعه: پرداخت اپلیکیشن فی

TOPIC 20: AI AND SYNTHETIC BIOLOGY

We develop machine learning based techniques for the design-build-test-learn loops in synthetic biology. The loops can collect relevant data automatically once properly set up, but designing the task and the loop, and iteratively the next loop based on what was learned, requires contribution from a human expert. The AI-assisted modelling and design methods which FCAI develops aim to give a new solution to this bottleneck task in biotechnology. In the work, we can combine machine learning expertise of FCAI with metabolic engineering and synthetic biology expertise of the Finnish Centre of Excellence in Molecular Engineering of Biosynthetic Hybrid Materials Research (HYBER).

  • Supervision: Samuel Kaski, (Aalto University), Merja Penttilä (VTT), Juho Rousu (Aalto University), Paula Jouhten (VTT)

  • Keywords: AI-assisted design, sequential experimental design, human-in-the-loop machine learning

  • Contribution to FCAI research programs: Interactive AI; Autonomous AI; Easy and privacy-preserving modeling tools; Simulator-based inference

  • Level: Postdoctoral researcher or research fellow

TOPIC 21: AI-ASSISTED MODELLING IN ECONOMICS

We are starting collaboration to address a few key questions in economics with new machine learning based approaches. Interesting topics include: 1. health economics: data-driven targeting of preventive treatments; 2. prior elicitation for economic models; 3. AI-assisted mechanism design and design of economic models. We will organize a supervision team consisting of experts from both the Finnish Center for Artificial Intelligence FCAI and Helsinki Graduate School of Economics GSE.

  • Supervision: A team from FCAI and Helsinki GSE; contact persons Samuel Kaski (FCAI, Aalto University) and Otto Toivanen (Helsinki GSE, Aalto University)

  • Keywords: Machine learning, AI-assisted design, economics

  • Contribution to FCAI research programs: Simulator-based inference; Interactive AI; Agile probabilistic AI

  • Level: Postdoctoral researcher or research fellow

TOPIC 22: DIGITAL TWINS FOR THE HUMAN BRAIN

We use machine learning techniques and the extensive existing data on brain structure and function to learn Digital Twins or brain processes for clinical use. They will be complemented with new data measured with new measurement techniques being developed in the Ilmoniemi group. AI-assisted modelling techniques will be used to design the measurements for particular tasks, and for personalizing the twins for patients.

  • Supervision: Samuel Kaski (Aalto University), Risto Ilmoniemi (Aalto University); with additional co-supervisors

  • Keywords: AI-assisted modelling and design, personalized medicine, neuroscience

  • Contribution to FCAI research programs: Agile probabilistic AI; Interactive AI; Simulator-based inference; Data-efficient deep learning

  • Level: Postdoctoral researcher or research fellow

About FCAI

Finnish Center for Artificial Intelligence FCAI brings together the world-class expertise of Aalto University and the University of Helsinki in AI research, strengthened further with VTT Technical Research Centre of Finland and an extensive set of companies and public sector partners. FCAI has been selected as one of the prestigious Flagships of the Academy of Finland, a status granted to very few selected centers of excellence with high societal impact. The total budget of FCAI is 250 M€ for the initial flagship term (2019-2026). FCAI was recently selected to host one of the first ELLIS (European Laboratory for Learning and Intelligent Systems) units that assemble European top talent in machine learning.

The Helsinki Metropolitan area forms a world-class information technology hub, attracting leading scientists and researchers in various fields of ICT and related disciplines. Moreover, as the birth place of Linux, and the home base of Nokia/Alcatel-Lucent/Bell Labs, F-Secure, Rovio, Supercell, Slush and numerous other technologies and innovations, Helsinki is becoming one of the leading technology startup hubs in Europe. Finland tops international rankings in education, equality, safety, and happiness, and Helsinki is regularly ranked as one of the most livable cities in the world.

HOST ORGANIZATIONS

Aalto University and the University of Helsinki form a globally top-ranked fundamental research cluster in AI. Moreover, VTT Technical Research Centre of Finland brings to FCAI its strong track record and networks for application of AI technologies to industrial and societal problems. All three host institutions are located in the Helsinki Metropolitan area. The employing university will be determined according to the supervising professor.

Job details

Depending on the primary supervisor, the position is based in either the Aalto University or the University of Helsinki. The contract is typically made for two years with the possibility to extend it by mutual agreement. For exceptional candidates, a longer term position can be considered.

The job details are negotiated on an individual basis. The salary for a newly graduated postdoctoral researcher starts typically from about 3500 EUR and for a PhD student from about 2500 EUR, depending on experience and qualifications. The persons hired will be covered by the Finnish national health insurance system and have access to the occupational health care services at university.

How to apply

The applications are to be submitted through the online electronic application system of Aalto University.

In the application form, please specify to which topic(s) outlined above you apply, and explain in the motivation letter how you could contribute to these selected topic(s). You do not have to write several motivation letters in case you apply for multiple projects, but if you prefer you can attach separate letters for individual projects.

Required attachments:

  • Motivation letter including a tentative plan for the research work (1–5 pages)

  • CV

  • List of publications (please do not attach full copies of publications)

  • A transcript of  studies and the degree certificate of the PhD/Master’s degree. If the degree is still pending, a plan for its completion must be provided.

  • Contact details of possible referees

All material should be submitted in English. The application materials will not be returned. Short-listed candidates will be invited for an interview which are conducted online.

The deadline for applications is October 5, 2020 (midnight UTC+02:00)

Questions? Please read all the above material carefully, and if your question is still unsolved, please contact the coordinating HR Secretary Sanni Kirmanen (firstname.lastname at aalto.fi)

Eligibility and qualifications

POSTDOCTORAL POSITIONS

The candidate should have or be close to having a PhD degree and is expected to have an excellent track record in scientific research in one or several fields relevant to the position. Good command of English is a necessary prerequisite. In the review process, particular emphasis is put on the quality of the candidate’s previous research and international experience, together with the substance, innovativeness, and feasibility of the research plan, and its relevance to the research programs or groups in question. Efficient and successful completion of studies is considered an additional merit.

DOCTORAL STUDENT POSITIONS

In the Finnish university system, a person must have a Master’s degree in order to enroll for doctoral studies. In case you wish to pursue graduate studies with a B.Sc. background, please apply first to one of the participating units’ Master’s programmes (Aalto University School of Science (SCI) or School of Electrical Engineering (ELEC), and University of Helsinki). A number of these programs provide special “doctoral tracks” with some financial support and study plans oriented towards continuing to doctoral education after the M.Sc. degree.

In order to get a study right for doctoral studies in Aalto University or University of Helsinki, an applicant with a Master’s degree outside of Aalto University/University of Helsinki needs to meet certain eligibility requirements and present some mandatory documents. A successful applicant must have an excellent command of Finnish, Swedish, or English. The universities participating in FCAI have strict language skills requirements for doctoral students (Aalto University, University of Helsinki). All international applicants applying for doctoral studies must demonstrate their proficiency in English. For example, an English language proficiency certificate (TOEFL, IELTS, CAE/CPE) is required later in case you will proceed to the recruitment process and apply for a doctoral study right. Only the following applicant groups can be exempted from the language test requirement: applicants who have completed a higher education degree 1) taught in Finnish, Swedish or English in a higher education institution in Finland or 2) in an English-medium programme at a higher education institution in an EU/EEA country, provided that all parts of the degree were completed in English or 3) an English-medium higher education degree requiring a physical on-site presence at a higher education institution in the United States, Canada, Great Britain, Ireland, Australia or New Zealand. More information on minimum language requirements and language test scores can be found at Master programmes admission webpage (see “Language requirements” and “demonstrating proficiency in English”).

Please, be prepared to check the eligibility requirements for doctoral studies and present additional documents in case you will proceed to the recruitment and apply for doctoral study right in Aalto University or University of Helsinki.

Please, find below more information about the eligibility requirements:

For the Aalto Doctoral Programme in Science (SCI)

For the Aalto Doctoral Programme in Electrical Engineering (ELEC)

For the University of Helsinki