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on algorithmic and application research in the domain of AI and scientific simulations to drive SW-HW-Technology co-optimization and develop novel ML/Hybrid-AI methods for semiconductor processing and health
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development. To integrate the image processing pipeline in a modular way, building on algorithms developed by your colleagues. To optimize performance and throughput and use software engineering best practices
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) mouse model to improve outcomes. A successful project will result in: A developed and optimized mouse EVLP platform. AI-based identification of 3 IRI and 3 rejection target genes expressed
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design, optimization, and triage Conduct in-depth SAR analysis, combining experimental and in silico data towards the design of therapeutics Apply and supervise structure- and ligand-based design
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resources, funding, and mentorship Your Responsibilities: Lead the design, development, and optimization of scalable AI inference pipelines Implement and experiment with LLMs, GNNs, multi-modal AI, and vision
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, specifically by critically evaluating and further optimizing existing components of the preprocessing pipeline, such as peak detection, retention time alignment, and data normalization. This will help reduce
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on equipment to ensure optimal performance. Implement advanced data processing tools, such as molecular networking (GNPS and other online platforms), into the existing workflow. You hold a degree in
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insights and models will support VUB in looking ahead, making informed choices, and acting strategically. Your responsibilities will include: Redesigning and optimizing the staff budgeting model to align
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and downstream processing, chassis strain engineering, and bioprocess optimization. Extensive experience working on tech transfer from lab-scale to pilot and commercial manufacturing
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for targeted antimicrobial efficacy. The project is structured into 4 work packages: • WP1: Fabrication of biocompatible electrospun scaffolds optimized for cellular interaction • WP2: Surface functionalization