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, conference presentations, and ultimately a PhD dissertation. The PhD thesis is to be completed within four years. You are also requested to teach. You will work with the following supervisory team: Dr. Frank
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an exceptional international team with expertise in all aspects of the project. Your tasks will include: • Preparation of different EO and in-situ datasets for training a machine learning model • Development of ML
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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be highly interdisciplinary. Two different profiles are possible for this position: either a profile in engineering sciences or biomedical physics, with a strong desire to learn about microbiology
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of the ecosystem to recover from perturbations that eventually lead the system to a different state. In such definition, vulnerability quantifies the degree of the forest ecosystem response to such external
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learning algorithms, and design of optical communication networks or power consumption and energy saving. The synergies of MATCH consortium act together to enable the thirteen DCs to become the next
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strengthen the data science and machine learning activities of the IAS-9 with exciting new topics. You will work in a multidisciplinary team of enthusiastic data scientists, software developers and domain
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aims to rethink how soft robots can interact with their environment, focusing on large-area, multi-point contacts—similar to how an elephant wraps its trunk around an object. Unlike traditional robots