The NWO gravitation project Stress in Action capitalizes on fast technological advances and big data analytics to move stress research from the lab to daily life. You will be part of the data analytic support core (DASC) team. The DASC will develop a variety of big data analytics approaches. Specific analytical questions for DASC include: (1) How can we derive counterfactual predictions of stress outcomes with multiple time-varying stress exposures? More specifically, you will work on novel combinations of joint models for longitudinal and time-to-event data with machine learning techniques.

Selection for third-cycle studies is based on the student's potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Consideration will be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme. Technology and society is a third-cycle subject that encompasses multidisciplinary and interdisciplinary studies of technology's role, interplay and importance in different sectors of society. The position is linked to a highly interdisciplinary research project that explores how AI transparency relates to consumer trust.

The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) is a member of the Helmholtz Association (HGF) and funded by federal and state government. AWI focuses on polar and marine research in a variety of disciplines such as biology, oceanography, geology, geochemistry and geophysics thus allowing multidisciplinary approaches to scientific goals. Background Climate models are essential for understanding complex physical and biogeochemical interactions across the planet. However, these models rely on many parameters that are necessary to accurately describe the effects of small-scale structures and processes that cannot be directly simulated. The project's goal is to develop data-driven methods of accurate, efficient parameter estimation in climate models.

We are seeking outstanding candidates with strong analytical and problem solving skills, who are strong in written and oral communication (in English), and have documented experience in the development of complex compute systems. The applicant should have provable skills in the state-of-the-art web-development frameworks, virtualization techniques as well as database technologies. Expertise in clinical data science and machine learning, as well as computer security and data privacy are welcome. A large roadblock of medical research is the difficult access to sensitive data which therefore hinders the training of complex and powerful machine learning concepts. This issue is amplified when considering rare diseases with low incidence numbers per hospital.

Do you recognize yourself in the job profile? Then we look forward to receiving your application by 15 February 2022. You can apply online by using the link below. Please mention the months (not just years) in your CV when referring to your education and work experience. Are you excited about creating a digital twin of the 3D world around you?

As a PhD candidate, you will primarily address the following four topics: (1) planning & scheduling (2) prescriptive quality (3) predictive maintenance and (4) hybrid intelligence. These PhD positions are part of the Green Transport Delta, a public-private innovation programme (funded by the Dutch Ministry of Economic Affairs and Climate) that aims to make Dutch transport sectors futureproof and sustainable. You will be embedded in the consortium around electrification, which focuses on improving various aspects of battery-powered electric transport as a key component of the transition to climate-neutral mobility. In a joint collaboration with VDL Nedcar, the largest Dutch automotive manufacturing company, you will work in a team to investigate AI techniques within VDL Nedcar's manufacturing environment. The ultimate goal is to make intelligent decisions in a transparent and reliable way, reduce costs, and save energy and reduce overall CO2 emissions.

This position requires a Master's degree or equivalent in Computer Science, Mathematics & Computing, or Engineering. Candidates in the final phase of their Master study may apply. A successful candidate should have a strong interest in at least one of the following topics: fundamental machine learning, neural network architecture, artificial intelligence, and interpretable learning. Since our research results are evaluated experimentally, good programming and system research skills are necessary. Applicants must document fluency of in English and be able to work in an international environment.

All employees are expected to contribute to excellence through high quality research and teaching. The working environment for this position will be at CBU, in the Systems Biology & Machine Learning group headed by Prof. Tom Michoel. The aim of the INTRePID project is to create intelligent systems for personalized and precise risk prediction and diagnosis of non-communicable diseases using multi-omics data, by developing, implementing and validating novel algorithms for structure learning and inference in large-scale, multi-organ causal Bayesian gene networks. The project will have privileged access to a unique resource of multi-omics data from four Nordic countries generated by the project partners for a proof-of-concept application in cardiovascular medicine. The person appointed on this position will develop and apply models and algorithms for causal inference, graph representation learning, and inference in large-scale Bayesian networks.

How do we deal with very large data sets of high resolution images, in particular in the field of radio astronomy? This question encompasses the scope of a joint PhD project between the University of Groningen (The Netherlands), the University of Stellenbosch (South Africa), and ASTRON, which is the Netherlands Institute for Radio Astronomy. Modern radio telescopes typically consist of 100 to a few hundred receiving elements, whose signals are pairwise correlated producing tens of thousands correlations for tens of thousands of frequency channels simultaneously. For a system like the Square Kilometre Array (SKA) this produces a data deluge of 1 TByte/s. This data may be affected by man-made radio frequency interference (RFI), instrumental failures and other effects that make the data unsuitable for scientific analysis.

TU Delft is a top tier university and is exceedingly active in the field of Artificial Intelligence. The AIFluids lab was recently established to foster the use of AI in the Aerospace Sciences. Designing more efficient aircrafts and wind farms requires a deeper understanding of complex flows. The AIFluids Lab is focused on two major challenges of fluid mechanics: the prediction and the control of complex, transitional and turbulent flows. New experimental techniques and high-fidelity flow simulations are providing larger and more detailed datasets.