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Field
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this diversity. Our research spans comparative genomics, chromatin architecture, gene expression, protein abundance, and metabolite profiling—combined with computational biology, machine learning, and advanced
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for embedded and GPU platforms. Collaborate with ARSPECTRA engineers and surgeons to create a complete AR guidance pipeline : tracking, SLAM, gaze, user interface Your profile PhD in machine learning
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the genetic architecture of molecular traits alone and in interaction with the environment. S(h)e will help in defining the direction of scientific work, conducting research, analyzing data, preparing the work
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and work with talented PhD students to advance the state-of-the-art in nanomechanics, optomechanics and cavity quantum electrodynamics. This is a highly multidisciplinary project involving
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contract Category (A,B or C) : A Contract/project period : Expected date of employment : March/April 2026 Proportion of work : 100% Desired level of education : PhD (Doctorate
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platform Who we are looking for Required qualifications: Applicants must hold a PhD degree awarded no more than three years prior to the application deadline* in: Electrical engineering Engineering physics
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concerned the conversion of (thio)esters into (thio)ethers via nickel-catalyzed intramolecular decarbonylation. This research axis was explored through a PhD project carried out in the laboratory and has
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-actively collaborate in a network of different research groups Willingness to work in a team and to mentor PhD students Strong written and verbal communication skills in English The following experience will
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primary research areas: 1) statistical inference in high-dimensional and large-scale testing scenarios; 2) the development of novel model architectures for large-scale proteomics data; and 3) causal
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design platforms for rapid deisgn and optimisation of novel targeting modules. Your responsibilities will include: Designing and implementing state-of-the-art deep learning architectures for protein