{"id":3826073,"url":"https://alion.io/job/keysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization","title":"Machine Learning — PhD Intern (Benchmarking for Inverse Design and Optimization)","company":{"id":53596,"name":"Keysight","domain":"keysight.com","url":"https://alion.io/company/keysight","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":null},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"C++","optional":true},{"name":"CMake","optional":true},{"name":"Git","optional":true},{"name":"Transfer Learning","optional":true}],"status":"live","first_seen_at":"2026-11-05T16:30:00Z","employer_posted_date":"2026-11-05","last_verified_at":"2026-11-06T13:07:11Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"OverviewKeysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do.\nOur award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.\nAbout the Program\nKeysight's AI team develops machine learning and optimization methods for engineering problems where simulation, measurement, and intelligent design workflows interact.\nThis internship focuses on rigorous benchmarking of machine learning and optimization methods for inverse design and optimization. Meaningful comparisons require more than running different algorithms on the same problem: objectives, constraints, initialization, evaluation budgets, simulation settings, and statistical procedures must be controlled carefully.\nYou will develop a reproducible benchmarking framework for comparing an existing engineering design workflow with selected academic and open-source methods across representative inverse design and optimization problems.\nRole Overview\nAs a PhD Intern in Machine Learning and Optimization, you will design, implement, and evaluate a benchmark suite covering a small number of representative engineering design problems.\nThe work spans the full benchmarking process: reviewing relevant research, selecting strong comparison methods, defining fair experimental protocols, integrating available implementations, automating simulation-based experiments, and analyzing where different approaches succeed or fail.\nA central objective will be to distinguish meaningful algorithmic differences from effects caused by evaluation budget, initialization, training cost, randomness, or implementation choices. You will measure both the quality of the final engineering solution and the computational or simulation effort required to obtain it.\nThe internship will focus on two agreed engineering use cases and approximately two or three carefully selected baseline methods. The goal is depth and experimental rigor rather than a broad but shallow algorithm survey.\nBy the end of the internship, you will deliver a reproducible benchmarking workflow, quantitative comparison results, and an evidence-based technical report describing performance, robustness, limitations, and areas requiring further evaluation.\nYou will work alongside machine learning researchers, optimization experts, and simulation engineers.\nLearning Outcomes\nApply modern optimization and machine learning methods to realistic engineering design problems.\nLearn how to design statistically sound benchmarks for simulation-based optimization and inverse design.\nDevelop practical understanding of sample efficiency, robustness, convergence behavior, and solution quality.\nLearn how evaluation budgets and experimental protocol choices can materially affect conclusions about algorithm performance.\nGain experience assessing research methods for reproducibility, applicability, and engineering relevance.\nBuild working fluency with simulation-driven optimization and engineering design workflows.\nDevelop experience automating large experimental studies and communicating uncertainty and limitations clearly.\nTake a benchmark methodology from research experiments to a reusable engineering evaluation framework.\nResponsibilities\nReview relevant state-of-the-art research and available open-source implementations in inverse design, black-box optimization, reinforcement learning, and related methods as appropriate to the selected use cases.\nSelect a small set of technically meaningful baseline methods together with the supervising researchers.\nDefine representative engineering optimization problems with explicit design variables, objectives, constraints, parameter ranges, and simulation settings.\nEstablish matched experimental protocols covering initialization, random seeds, evaluation budgets, stopping criteria, constraint handling, and training or model-building costs.\nAdapt and integrate baseline implementations with existing engineering simulation workflows.\nAutomate repeated experiments using reproducible scripts and configuration-driven workflows.\nMeasure final design quality under fixed engineering evaluation budgets.\nQuantify sample efficiency, including the number of simulation or engineering evaluations required to reach defined performance targets.\nCompare target satisfaction, objective quality, constraint violations, wall-clock cost, and variability across repeated runs.\nAccount explicitly for initialization, surrogate-model fitting, policy training, or other algorithm-specific costs where relevant.\nEvaluate robustness across random seeds, problem variations, noisy observations, and constrained design spaces.\nInvestigate where methods perform consistently, where results become unstable, and where conclusions depend strongly on the selected problem or budget.\nEvaluate reuse or transfer of information across related design problems where appropriate.\nRecord configurations, software versions, random seeds, datasets, and experimental results so that comparisons can be reproduced.\nProduce reusable benchmark scripts, analysis tools, plots, and tables.\nDocument software sources, dependencies, and licensing information for internal review.\nPresent findings with clear separation between experimentally supported conclusions, observed limitations, and areas requiring further study.\nQualifications\nRequired Qualifications\nCurrently pursuing or recently completed a PhD in Machine Learning, Computer Science, Electrical or Electronic Engineering, Applied Mathematics, or a related technical field.\nStrong Python programming skills, including scientific computing, data analysis, and experiment automation.\nStrong understanding of numerical optimization, machine learning, experimental design, and statistical evaluation.\nExperience implementing, adapting, or evaluating optimization or machine learning algorithms beyond tutorial-scale examples.\nFamiliarity with at least one relevant class of methods such as Bayesian optimization, evolutionary or global optimization, surrogate-assisted optimization, policy optimization, or model-based reinforcement learning.\nAbility to read research papers critically, understand methodological assumptions, and translate published methods into reproducible experiments.\nExperience with repeated experimental evaluation, multiple random seeds, controlled comparisons, and quantitative analysis.\nWorking knowledge of probability, statistics, and the interpretation of variability across experimental runs.\nExperimental discipline, including careful baselines, reproducibility, experiment tracking, and transparent reporting of inconclusive or negative results.\nWillingness to understand the underlying engineering problem rather than treat it as an abstract optimization benchmark. Mentorship in the domain will be provided.\nClear technical communication and the ability to collaborate with researchers and engineers from different technical backgrounds.\nPreferred Qualifications\nExperience with Bayesian optimization, evolutionary strategies, global optimization, surrogate-assisted optimization, or reinforcement learning for continuous control or design.\nExperience evaluating sample efficiency, transfer learning, model-based reinforcement learning, or related sequential optimization methods.\nExposure to inverse design and optimization, RF or microwave engineering, electromagnetic component design, semiconductor design, or test and measurement.\nExperience with engineering simulation environments and their scripting interfaces.\nExperience adapting open-source research code and managing repeated computational experiments.\nFamiliarity with constrained or noisy black-box optimization.\nExperience with scientific machine learning or simulation-driven machine learning.\nFamiliarity with C++ or integrating Python research workflows with compiled software.\nExperience with CMake, profiling, or production-oriented numerical software is a plus.\nPrerequisites\nFamiliarity with Git, modular software development, and reproducible experiment workflows.\nComfort working with existing research code and simulation interfaces rather than developing exclusively from clean-room examples.\nUnderstanding of experiment tracking, statistical comparison, and numerical reproducibility.\nAbility to design an evaluation that answers a technical question rather than simply produces a favorable number.\nAbility to communicate assumptions, limitations, and uncertainty clearly.\nReview relevant state-of-the-art research and available open-source implementations in inverse design, black-box optimization, reinforcement learning, and related methods as appropriate to the selected use cases.\nSelect a small set of technically meaningful baseline methods together with the supervising researchers.\nDefine representative engineering optimization problems with explicit design variables, objectives, constraints, parameter ranges, and simulation settings.\nEstablish matched experimental protocols covering initialization, random seeds, evaluation budgets, stopping criteria, constraint handling, and training or model-building costs.\nAdapt and integrate baseline implementations with existing engineering simulation workflows.\nAutomate repeated experiments using reproducible scripts and configuration-driven workflows.\nMeasure final design quality under fixed engineering evaluation budgets.\nQuantify sample efficiency, including the number of simulation or engineering evaluations required to reach defined performance targets.\nCompare target satisfaction, objective quality, constraint violations, wall-clock cost, and variability across repeated runs.\nAccount explicitly for initialization, surrogate-model fitting, policy training, or other algorithm-specific costs where relevant.\nEvaluate robustness across random seeds, problem variations, noisy observations, and constrained design spaces.\nInvestigate where methods perform consistently, where results become unstable, and where conclusions depend strongly on the selected problem or budget.\nEvaluate reuse or transfer of information across related design problems where appropriate.\nRecord configurations, software versions, random seeds, datasets, and experimental results so that comparisons can be reproduced.\nProduce reusable benchmark scripts, analysis tools, plots, and tables.\nDocument software sources, dependencies, and licensing information for internal review.\nPresent findings with clear separation between experimentally supported conclusions, observed limitations, and areas requiring further study.\nRequired Qualifications\nCurrently pursuing or recently completed a PhD in Machine Learning, Computer Science, Electrical or Electronic Engineering, Applied Mathematics, or a related technical field.\nStrong Python programming skills, including scientific computing, data analysis, and experiment automation.\nStrong understanding of numerical optimization, machine learning, experimental design, and statistical evaluation.\nExperience implementing, adapting, or evaluating optimization or machine learning algorithms beyond tutorial-scale examples.\nFamiliarity with at least one relevant class of methods such as Bayesian optimization, evolutionary or global optimization, surrogate-assisted optimization, policy optimization, or model-based reinforcement learning.\nAbility to read research papers critically, understand methodological assumptions, and translate published methods into reproducible experiments.\nExperience with repeated experimental evaluati...","description_format":"text","description_chars":14148,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["EDA Software","Simulation & Digital Twin Software","Test & Measurement Instruments"],"lifecycle":[{"event":"open","at":"2026-11-05T18:28:22Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":44,"reasons":["conf:40","velocity","win:early","comp:junior"],"computed_at":"2026-11-08T05:47:12Z"},"pay":null,"html_url":"https://alion.io/job/keysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization","json_url":"https://alion.io/job/keysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization.json","meta":{"generated_at":"2026-11-09T04:10:56Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":3181,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-10T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.1,"usd":0.01,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":53596},"rest":"https://alion.io/mcp/rest/get_company?id=53596"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fkeysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":2,"usd":0.2},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fkeysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.1,"usd":0.01},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fkeysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/keysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fkeysight-machine-learning-phd-intern-benchmarking-for-inverse-design-and-optimization"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3826073}},{"tool":"search_jobs","arguments":{"company":"keysight.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3826073},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. Docs: https://alion.io/developers"}}