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at the Faculty of Medicine. Clinical Sciences Lund cooperates closely with Skåne University Hospital and the Faculty of Medicine in order to optimize the conditions for preclinical and clinical translational
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microscopy on magnetic materials and/or the use and development of coherent x-ray microscopy techniques, to join the SoftiMAX team. As part of the team, you will ensure optimal operation of the beamline plus
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materials, formulating and optimizing electrolyte systems (including hybrid solvents, functional additives, and water-in-salt electrolytes), and investigating the electrochemical performances of designed
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optimization and functional characterization of small molecules Integrating biophysical, computational, and cell‑based data Independent experimental design, data analysis, and interpretation Active participation
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innovative approaches to design and optimize materials with enhanced bioactivity. Experience in design and synthesis of peptides will be considered a strong merit, as well as expertise in chemical modification
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: - Distributed robotic autonomy for embodied intelligence - Data-driven and learning-based control - Decentralized decision-making and distributed optimization - Belief-space and uncertainty-aware planning - Neuro
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in clinically relevant environments. Key work assignments include: Design, fabrication, and optimization of high-performance plasmonic nanostructures and SERS substrates for sensing in complex
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with FPGA programming used in superconducting quantum circuit experiments. Model-Free Quantum Control via Reinforcement Learning: Reinforcement learning (RL) is an emerging approach for optimizing
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important examples such as Schrödinger Cat states, which are superpositions of coherent states with different phases. This project focuses on the optimal preparation of Schrödinger Cat states in a propagating
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as