129 coding-"https:"-"https:"-"https:"-"KTH" positions at Pennsylvania State University
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Bachelor’s degree in Psychology, Neuroscience, or a related field Experience in a lab that conducts behavioral coding, neuroimaging, or eye-tracking studies Research experience with children Important
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environments Hardware‑in‑the‑loop (HIL) environments or lab‑based hardware integration Use of AI‑ or LLM‑assisted workflows to improve software development efficiency, code quality, or documentation Agile
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requirements, and validation methods such as stray light, thermal performance, MTF, distortion, and vignetting Optical design and analysis software (e.g., Zemax, CODE V, TracePro, FRED) Current coating and
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emerging SD-WAN technologies including SASE, zero-trust architectures, and cloud-integrated networking Implement network automation frameworks using Python, Ansible, and infrastructure-as-code tools Deploy
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controlled substances (49 CFR PART 40), and physical examination requirements as per PA. CODE 231.85. Penn State does not sponsor or take over sponsorship of a staff employment Visa. Applicants must be
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of infrastructure code Configure and optimize various compute resources and networks including bare metal and virtualized/container environments Provide demonstrations of your work to the internal team and
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for simulation verification and validation, including identification of appropriate metrics Document algorithms, code designs, and software tests in accordance with modern software development practices Additional
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for simulation verification and validation, including identification of appropriate metrics Document algorithms, code designs, and software tests in accordance with modern software development practices Perform
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evolutionary biology. Produce and maintain well-documented code, notebooks, workflows, and other research outputs Provide weekly progress updates during scheduled meetings with mentoring research staff Prepare
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products, models, code, and documentation. Stay up-to-date with the latest advancements in machine learning, AI for biology, and the rapidly evolving landscape of public molecular and cellular datasets