469 programming-"the"-"DAAD"-"IMPRS-ML"-"EURAXESS"-"O.P"-"Aalborg-University"-"UCL" positions in Denmark
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-translation and AI-driven data analyses. We value on-campus collaboration: plan for at least three days per week at the department; short research stays elsewhere can be agreed when useful for the project. Your
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are looking for candidates who have experience with developing AI or machine learning models, as well as bacterial sequence analysis. You should be familiar with relevant programming languages such as Python
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Researcher Development Programme targeted at career development for postdocs at AU. You can read more about it here . The application must be submitted via Aarhus University’s recruitment system, which can be
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published/in manuscript form respectively). A research plan for the next three years and plans for external funding, including for instance interdisciplinary collaboration and didactic innovation. A teaching
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. Knowledge of olfaction, calcium, or GPCR signalling is preferred. Experience with biological assay developments such as bulk and single-cell reporter assays. Programming skills (e.g., Python, R). As a formal
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. in the graduation class for the study programme. List of publications and maximum 2 examples of relevant publications (in case you have any publications). References may be included, you're welcome
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) or a similar degree with an academic level equivalent to a two-year master's degree.[BN3] [SK4] Strong programming skills in Python and MATLAB Background in biomedical signal processing, ML, and BCIs
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PhD program, you must include a signed letter from your PhD supervisor stating your expected date of completion. Strong collaborative and communication skills and an interest in method development and
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at DTU Compute, including teaching and supervision activities We are looking for you who have: Solid knowledge of process mining, continual learning, or cybersecurity Strong analytical and programming
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equivalent). Other qualifications and competences include: Experience with bioinformatics Experience with mathematical modelling and programming, including source attribution modelling of foodborne pathogens