{"id":2337873,"url":"https://alion.io/job/siemens-phd-position-ai-driven-computational-models-for-battery-degradation","title":"PhD Position: AI-driven computational models for battery degradation","company":{"id":131,"name":"Siemens","domain":"siemens.com","url":"https://alion.io/company/siemens","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Avature","truth_index":{"grade":"A","score":100,"open_postings":12,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Early Careers","role_family":"Early Careers","seniority":null,"employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Leuven, Belgium"],"countries":["BE"],"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":"Digital Twin","optional":false},{"name":"Machine Learning","optional":false}],"status":"live","first_seen_at":"2026-10-12T10:31:07Z","employer_posted_date":"2026-10-12","last_verified_at":"2026-10-22T04:24:35Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"PhD Position in AI-driven computational models for battery degradation\nAs part of the recently launched MSCA-DN (Marie Skłodowska-Curie Actions Doctoral Network) project funded by the European Union, titled TRAIN-FAST (Training Researchers in Adaptive, Integrated and Safe Technologies for Ultra-Fast Battery Charging), Siemens Industry Software NV (SISW) has a PhD position to collaborate in the development of a Digital Twin for predictive ultra-fast charging. This PhD is in collaboration with Politecnico di Torino (POLITO) where the PhD degree will be awarded.\nTransportation accounts for approximately one-third of global energy-related carbon dioxide emissions and is undergoing a profound transition towards electric mobility and renewable energy integration. Ultra-fast charging technology is critical for providing charging convenience comparable to conventional refuelling and for accelerating the widespread adoption of electric vehicles. The aim of TRAIN-FAST is to educate the next generation of leading scientists and engineers who will advance ultra-fast charging technology within the broader electric-mobility ecosystem. The programme adopts an interdisciplinary approach, combining advanced engineering, data science, techno-economic analysis, and socio-behavioural research to address complex cross-sector challenges. As part of this project, you will have the opportunity to conduct pioneering research at the forefront of battery and charging technology. Please refer to the TRAIN-FAST project website (https://train-fast.dtu.dk/) for further information about the project and the PhD opportunities available at the other recruiting organisations.\nResponsibilities and qualifications\nThe PhD researcher will focus on the following activities:\nDevelop a multi-scale and multi-physics simulation workflow to accelerate battery material screening and cell design capturing coupled electro-chemical, thermal, and mechanical degradation.\nEmploy a combination of ML (Machine Learning) and MOR (Model Order Reduction) techniques to efficiently approximate coupled multi-physics partial differential equations across high-dimensional parameter spaces.\nThese activities will be carried out in close collaboration with the other PhD candidates within the TRAIN-FAST network and with strong support from the industrial partners.\nYou should have a passion for research in industrial context and a background in modeling and simulation in multi-physics domains (especially in electro-chemistry and mechanical). You will become part of a multidisciplinary innovation team located in Leuven (Belgium) with a focus on energy topics such as batteries, hydrogen, and covering several application fields from automotive to aerospace. We combine these innovation themes with numerical methods such as MOR and ML in close collaboration with academia.\nYour specific tasks will cover:\nDevelop physics-based coupled electro-chemical and mechanical models for batteries.\nInvestigate and derive degradation-related phenomena for specific battery chemistries.\nDevelop workflows linking physics-based models to light/fast models needed to support control strategies by operator-learning as ML-accelerated ROM with active learning and Bayesian optimisation guiding exploration of the high-dimensional parameter spaces\nEvaluate and validate numerical models and methods against experimental data and results reported in the scientific literature.\nDisseminate research findings through scientific publications and presentations at international conferences.\nYou are expected to take an active role in the international TRAIN-FAST network, participate in secondments at partner organisations, and engage in the network's joint training activities, workshops, and conferences.\nEligibility criteria\nTwo-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. \nExperience with numerical modelling or related topics will be advantageous. \nStrong communication skills in English, both written and oral, are essential.\nResearchers must be doctoral candidates, i.e. not already in possession of a doctoral degree. You may apply prior to ob­tai­ning your master's degree but cannot begin before having received it. Applicants of any nationality are welcome.\n Note that the MSCA-DN mobility clause applies: the applicant must not have resided or carried out their main activity (work, studies, etc.) in Belgium for more than 12 months in the 36 months immediately before the date of recruitment. Compulsory national service, short stays such as holidays, and time spent as part of a procedure for obtaining refugee status under the Geneva Convention are not taken into account.\nThe PhD application is subject to academic approval, and the candidate will be enrolled in a PhD programme at POLITO. For information about our enrolment requirements and the general planning of the PhD study programme, please seethe specific rules at POLITO https://www.polito.it/en/education/phd-programmes-and-postgraduate-school/admissions-to-phd-programmes/enrolment/enrolling). \nWhy Working at SISW?\nWe are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.\nAdditional information\nRecruitment: October 2026 - February 2027\nStart date: April 2027\nDuration: 36 months\nThe doctoral candidate will be employed by Siemens Industry Software NV for the full duration\nThe doctoral candidate will receive an attractive salary in accordance with MSCA regulations, consisting of (monthly):\na gross living allowance of €4010 (country correction coefficient applies) including salary, tax and insurance;\na mobility allowance of €710;\nif applicable, a family allowance of €660.\nThe exact (net) salary will be confirmed upon appointment.\nApplication procedure\nYour complete online application must be submitted no later than January 2027\nApplications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link \"Apply now\", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:\nA letter motivating the application (cover letter)\nCurriculum vitae \nGrade transcripts and BSc/MSc diploma (in English) including official description of grading scale\nApplications received after the deadline will not be considered.\nSiemens Digital Industry Software NV evolved from LMS International, one of the first spin-offs of KU Leuven, and now operates as the Simulation and Test Solutions division (SIM) within Siemens Digital Industries Software (DI SW), a recognized leader in Product Lifecycle Management (PLM). Through the Simcenter portfolio and Xcelerator platform, SISW provides a comprehensive range of digital and test solutions for vital economic industries such as manufacturing, energy and transportation. Our technologies combine the physical and digital worlds in a continuous flow of data across the entire product lifecycle, from initial concept to deployment and operation.\nTransform the everyday and shape the future of your career with us\nSiemens Digital Industries Software is your place to thrive. Whether you’re a current student, recent graduate, or postgraduate, our talent hiring programs offer the trust and freedom to succeed while building a strong foundation for your professional career. You will have the chance to work on real projects, contribute to our future success, and make an impact for our customers.\n#LI-PLM\n#LI-GV1\n#LI-Hybrid","description_format":"text","description_chars":7691,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[{"name":"Belgium","iso":"BE","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Rail Transport","Medical Imaging Systems","Building Automation & Controls","Manufacturing & Industrial Software"],"lifecycle":[{"event":"open","at":"2026-10-12T10:31:07Z"}],"visa":[],"liveness":{"score":23,"band":"cold","label":"Long shot","p_open":1,"p_active":0.51,"p_room":0.45,"age_days":9,"expected_fill_days":6,"reasons":["conf:1","velocity","win:tail","comp:brand"],"computed_at":"2026-10-22T06:10:10Z"},"pay":null,"html_url":"https://alion.io/job/siemens-phd-position-ai-driven-computational-models-for-battery-degradation","json_url":"https://alion.io/job/siemens-phd-position-ai-driven-computational-models-for-battery-degradation.json","meta":{"generated_at":"2026-10-22T06:29:04Z","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":1425,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-23T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"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":131},"rest":"https://alion.io/mcp/rest/get_company?id=131"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fsiemens-phd-position-ai-driven-computational-models-for-battery-degradation"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"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%2Fsiemens-phd-position-ai-driven-computational-models-for-battery-degradation"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"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%2Fsiemens-phd-position-ai-driven-computational-models-for-battery-degradation"},{"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/siemens-phd-position-ai-driven-computational-models-for-battery-degradation\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fsiemens-phd-position-ai-driven-computational-models-for-battery-degradation"}]}