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Field
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Are you eager to support exciting research with ultracold atoms and trapped ions? Do you want to participate in the creation of complex machines that have never existed before? Do you enjoy sharing
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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to optimization problems with possible topics covering: Variational quantum algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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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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strong research capabilities with a deep understanding of trading to design, validate, backtest, and implement statistical and advanced machine learning models. Your work will span a range of initiatives
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qualification you must hold a PhD degree (or equivalent). Specifically, a PhD in manufacturing engineering (or equivalent) with documented experience in the following areas: Precision machining Machining system
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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knowledge of wireless communications, and signal processing. You have at least intermediary knowledge of machine learning algorithms, including federated learning, split learning, and graph neural networks
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engineering, data science, or related fields Strong programming skills (especially in Python), and experience with simulation, modelling, data analysis, machine learning, and hardware control Solid