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Nordisk. The project will explore the correlation between gene dosage, stability and productivity. Further, different approaches aimed at stabilizing genetic constructs, such as genome integrations
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, satellite altimetry, ice flow maps and terminus positions and other relevant data to constrain numerical model to simulate 1900-present and future (present-2100) ice flow changes under different UN IPCC
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multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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Experience with VLSI design (Cadence tools, Verilog/VHDL, SPICE) Knowledge of neural networks and neuromorphic systems is a strong advantage Good programming skills (e.g., Python, MATLAB) and interest in