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it will also involve a small degree of teaching and supervision. To that end, the successful applicant will be expected to take part in the department’s teaching and supervision activities and to teach
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for public policy, and the relationship between business and democracy. We have a large group of faculty committed to examining the role of money in politics and non-market strategy. Faculty apply advanced
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considerations. Experience working with machine learning methods for control, perception, or decision-making in physical systems is an advantage. Knowledge of or a passion for sustainable computing
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neuro-adaptability with changes in cortical manifestations during an intervention (e.g., non-invasive brain stimulation) for symptom reduction. Large-scale data analysis (e.g. machine-learning) will
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interest and documented skills and experience in using computer-based tools to analyse, simulate and predict capture performance of active and passive fishing gears. A track record of publishing in peer
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collaborate with PhD and other post doctoral fellows, in cross-disciplinary collaborations with other research groups both national and international as well as with industrial partners. Qualifications A PhD
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. At the department we are approx. 670 academic employees, 500 PhD students and 160 technical/administrative employees who are cooperating across disciplines. As a postdoctoral research fellow you will be working
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the potential of biological systems. Big data approaches and analysis of biological systems are key research instruments at the Center. DTU Biosustain utilizes these advances for microbial cell factory design to
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: www.jura.ku.dk . The Faculty actively supports efforts to learn Danish. Qualification requirements Employment as a Postdoc requires academic qualifications at PhD level. More information on careers at UCPH and the
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qualification, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning