{"data":{"id":46070,"slug":"doktorand-i-hardoptimering-med-maskininlarning","type":"job","title":"PhD Candidate in Core Optimization with Machine Learning","description":"Do you want to work with machine learning, optimization, and reactor physics, supported by competent and friendly colleagues in an international environment? Do you want to contribute to the development of advanced computational methods for future nuclear power systems? Do you want an employer that invests in sustainable employee relations and offers secure, favorable working conditions? We welcome your application for a PhD position at Uppsala University.\n\nAs a PhD candidate, you will be part of a research group working on reactor physics, fuel cycle analysis, and computational methods for core and fuel optimization. The group combines physics-based computational models with modern optimization and data analysis methods. The work environment is international and multidisciplinary, with close links between fundamental method development and technically relevant applications.\n\nThe project is a continuation of an ongoing PhD project on core and fuel optimization for small modular reactors (SMR) within the competence center ANItA (Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future). The competence center brings together academia and industry to strengthen Swedish nuclear technology expertise and contribute to a sustainable energy transition. The previous PhD project has developed methods for optimizing equilibrium cycles, where the goal is to find recurring fuel management strategies that provide good fuel economy while meeting reactor physics safety margins. Special focus has been on combining advanced optimization algorithms with machine learning-based surrogate models, including graph-based representations of core loading patterns.\n\nYou will further develop this research direction. The project may include cycle-to-cycle optimization, development of new machine learning models, improved optimization strategies, uncertainty quantification, more efficient handling of physical constraints, and expanded analysis of fuel design, loading patterns, and safety-related quantities. The goal is to develop methods that enable faster and more reliable exploration of large design spaces in core and fuel optimization.\n\nDuties\nThe duties consist mainly of doctoral studies, where you conduct research within the project and complete courses as part of your doctoral education. The work involves developing, implementing, and evaluating computational methods for core and fuel optimization using machine learning and optimization algorithms.\n\nYour duties include:\n- Develop and apply machine learning-based surrogate models for reactor physics calculations,\n- Develop and evaluate optimization methods for fuel loading patterns and fuel composition,\n- Analyze safety-related parameters such as reactivity, power distributions, fuel utilization, and margins to technical limits,\n- Work with large datasets from reactor physics simulations,\n- Implement and document computational tools, for example in Python,\n- Compile and publish research results in scientific articles,\n- Present results at national and international conferences,\n- Participate in research group seminars, project meetings, and other scientific activities.\n\nTeaching and other institutional service may be included with a maximum of 20 percent of full-time work.\n\nQualifications\nTo be eligible for doctoral studies, you must have:\n- Completed an advanced degree in engineering physics, nuclear engineering, energy engineering, machine learning, computer science, applied mathematics, or another field relevant to the project, or\n- Completed at least 240 credits, of which at least 60 credits at advanced level including an independent thesis of at least 15 credits, or\n- Acquired equivalent knowledge in another way.\n\nFor the position, the following is also required:\n- Strong knowledge in physics, numerical methods, and\/or machine learning,\n- Good programming skills, for example in Python, Julia, C++, or equivalent,\n- Ability to work independently and in a structured manner,\n- Good ability to collaborate,\n- Good ability to express yourself orally and in writing in English.\n\nGreat weight will be placed on personal qualities such as analytical ability, initiative, accuracy, and motivation to conduct research within a multidisciplinary field.\n\nDesirable\/Merits\nIt is meritorious to have experience in one or more of the following areas:\n- Reactor physics, nuclear engineering, or neutron transport,\n- Core optimization, fuel cycle analysis, or fuel management,\n- Machine learning, especially neural networks, graph neural networks, or surrogate modeling,\n- Optimization algorithms, such as evolutionary algorithms, stochastic optimization, or multi-objective optimization,\n- Uncertainty quantification or statistical modeling,\n- Work with scientific computing software and high-performance computing,\n- Experience with version control and reproducible computing workflows.\n\nRegulations for PhD candidates are found in the Higher Education Ordinance Chapter 5 \u00a7\u00a7 1-7 and in the university's rules and guidelines.\n\nAbout the Application\nPlease attach transcripts, a copy of your thesis, and any other documents you wish to submit.\n\nAbout the Position\nThe position is time-limited, according to the Higher Education Ordinance Chapter 5 \u00a7 7. Full-time. Start date: January 1, 2027, or by agreement. Location: Uppsala.\n\nInformation about the position is provided by: Andreas Solders, 018-471 26 31, andreas.solders@physics.uu.se\n\nIn this recruitment, we have replaced the personal letter with questions that you answer as part of your application. Your answers will be used as part of the selection process.\n\nWelcome to submit your application no later than September 30, 2026, UFV-PA 2026\/2129\n\nNote that this is an abbreviated version of the announcement. To see the full announcement, please click on \"Apply here\" or visit Uppsala University's job announcement website.","language":"sv","is_translated":true,"title_original":"Doktorand i h\u00e4rdoptimering med maskininl\u00e4rning","description_original":"Vill du arbeta med maskininl\u00e4rning, optimering och reaktorfysik, med st\u00f6d av kompetenta och trevliga kollegor i en internationell milj\u00f6? Vill du bidra till utvecklingen av avancerade ber\u00e4kningsmetoder f\u00f6r framtidens k\u00e4rnkraftssystem? Vill du ha en arbetsgivare som satsar p\u00e5 ett h\u00e5llbart medarbetarskap och erbjuder trygga, f\u00f6rm\u00e5nliga arbetsvillkor? V\u00e4lkommen att s\u00f6ka anst\u00e4llning som doktorand vid Uppsala universitet.\nSom doktorand kommer du att vara en del av en forskargrupp som arbetar med reaktorfysik, br\u00e4nslecykelanalys och ber\u00e4kningsmetoder f\u00f6r h\u00e4rd- och br\u00e4nsleoptimering. Gruppen kombinerar fysikbaserade ber\u00e4kningsmodeller med moderna optimerings- och dataanalysmetoder. Arbetsmilj\u00f6n \u00e4r internationell och tv\u00e4rvetenskaplig, med n\u00e4ra koppling mellan grundl\u00e4ggande metodutveckling och tekniskt relevanta till\u00e4mpningar.\nProjektet \u00e4r en forts\u00e4ttning p\u00e5 ett p\u00e5g\u00e5ende doktorandprojekt om h\u00e4rd- och br\u00e4nsleoptimering f\u00f6r sm\u00e5 modul\u00e4ra reaktorer, SMR, inom kompetenscentrumet ANItA (Academic-industrial Nuclear technology Initiative to Achieve a sustainable energy future). Kompetenscentrumet samlar akademi och industri f\u00f6r att st\u00e4rka svensk k\u00e4rnteknisk kompetens och bidra till en h\u00e5llbar energiomst\u00e4llning. Det tidigare doktorandprojektet har utvecklat metoder f\u00f6r optimering av j\u00e4mviktscykler, d\u00e4r m\u00e5let \u00e4r att hitta \u00e5terkommande br\u00e4nslehanteringsstrategier som ger god br\u00e4nsleekonomi samtidigt som reaktorfysikaliska s\u00e4kerhetsmarginaler uppfylls. S\u00e4rskilt fokus har legat p\u00e5 att kombinera avancerade optimeringsalgoritmer med maskininl\u00e4rningsbaserade surrogatmodeller, inklusive grafbaserade representationer av h\u00e4rdladdningsm\u00f6nster.\nDu kommer att vidareutveckla denna forskningsinriktning. Projektet kan till exempel omfatta cykel-till-cykel-optimering, utveckling av nya maskininl\u00e4rningsmodeller, f\u00f6rb\u00e4ttrade optimeringsstrategier, os\u00e4kerhetskvantifiering, effektivare hantering av fysikaliska begr\u00e4nsningar samt ut\u00f6kad analys av br\u00e4nsledesign, laddningsm\u00f6nster och s\u00e4kerhetsrelaterade storheter. M\u00e5let \u00e4r att ta fram metoder som g\u00f6r det m\u00f6jligt att snabbare och mer tillf\u00f6rlitligt utforska stora designrum inom h\u00e4rd- och br\u00e4nsleoptimering.\nArbetsuppgifter \nArbetsuppgifterna best\u00e5r huvudsakligen av forskarutbildning, d\u00e4r du bedriver forskning inom projektet och f\u00f6ljer kurser inom forskarutbildningen. Arbetet innefattar utveckling, implementering och utv\u00e4rdering av ber\u00e4kningsmetoder f\u00f6r h\u00e4rd- och br\u00e4nsleoptimering med hj\u00e4lp av maskininl\u00e4rning och optimeringsalgoritmer.\nI arbetsuppgifterna ing\u00e5r att:\nutveckla och till\u00e4mpa maskininl\u00e4rningsbaserade surrogatmodeller f\u00f6r reaktorfysikaliska ber\u00e4kningar,\nutveckla och utv\u00e4rdera optimeringsmetoder f\u00f6r br\u00e4nsleladdningsm\u00f6nster och br\u00e4nslesammans\u00e4ttning,\nanalysera s\u00e4kerhetsrelaterade parametrar s\u00e5som reaktivitet, effektf\u00f6rdelningar, br\u00e4nsleutnyttjande och marginaler till tekniska begr\u00e4nsningar,\narbeta med stora datam\u00e4ngder fr\u00e5n reaktorfysikaliska simuleringar,\nimplementera och dokumentera ber\u00e4kningsverktyg, exempelvis i Python,\nsammanst\u00e4lla och publicera forskningsresultat i vetenskapliga artiklar,\npresentera resultat vid nationella och internationella konferenser,\ndelta i forskargruppens seminarier, projektm\u00f6ten och \u00f6vriga vetenskapliga aktiviteter.\n\nUndervisning och annan institutionstj\u00e4nstg\u00f6ring kan komma att ing\u00e5 med h\u00f6gst 20 procent av heltid.\nKvalifikationskrav \nBeh\u00f6rig till utbildning p\u00e5 forskarniv\u00e5 \u00e4r den som har\navlagt examen p\u00e5 avancerad niv\u00e5 inom teknisk fysik, k\u00e4rnteknik, energiteknik, maskininl\u00e4rning, datavetenskap, till\u00e4mpad matematik eller annat f\u00f6r projektet relevant omr\u00e5de, eller\nfullgjort minst 240 h\u00f6gskolepo\u00e4ng, varav minst 60 h\u00f6gskolepo\u00e4ng p\u00e5 avancerad niv\u00e5 inklusive ett sj\u00e4lvst\u00e4ndigt arbete om minst 15 h\u00f6gskolepo\u00e4ng, eller\np\u00e5 n\u00e5got annat s\u00e4tt f\u00f6rv\u00e4rvat i huvudsak motsvarande kunskaper.\n\nF\u00f6r anst\u00e4llningen kr\u00e4vs dessutom:\ngoda kunskaper i fysik, numeriska metoder och\/eller maskininl\u00e4rning,\ngod programmeringsf\u00f6rm\u00e5ga, exempelvis i Python, Julia, C++ eller motsvarande,\ngod f\u00f6rm\u00e5ga att arbeta sj\u00e4lvst\u00e4ndigt och strukturerat,\ngod samarbetsf\u00f6rm\u00e5ga,\ngod f\u00f6rm\u00e5ga att uttrycka sig i tal och skrift p\u00e5 engelska.\n\nStor vikt kommer att l\u00e4ggas vid personliga egenskaper s\u00e5som analytisk f\u00f6rm\u00e5ga, initiativf\u00f6rm\u00e5ga, noggrannhet och motivation att bedriva forskarstudier inom ett tv\u00e4rvetenskapligt omr\u00e5de.\n\u00d6nskv\u00e4rt\/meriterande i \u00f6vrigt\nDet \u00e4r meriterande med erfarenhet av ett eller flera av f\u00f6ljande omr\u00e5den:\nreaktorfysik, k\u00e4rnteknik eller neutrontransport,\nh\u00e4rdoptimering, br\u00e4nslecykelanalys eller br\u00e4nslehantering,\nmaskininl\u00e4rning, s\u00e4rskilt neurala n\u00e4tverk, grafneurala n\u00e4tverk eller surrogatmodellering,\noptimeringsalgoritmer, exempelvis evolution\u00e4ra algoritmer, stokastisk optimering eller flerm\u00e5lsoptimering,\nos\u00e4kerhetskvantifiering eller statistisk modellering,\narbete med vetenskapliga ber\u00e4kningsprogram och h\u00f6gpresterande ber\u00e4kningar,\nerfarenhet av versionshantering och reproducerbara ber\u00e4kningsfl\u00f6den.\n\nBest\u00e4mmelser f\u00f6r doktorander \u00e5terfinns i H\u00f6gskolef\u00f6rordningen 5 kap \u00a7\u00a7 1\u20137 samt i universitetets regler och riktlinjer.\nOm ans\u00f6kan\nV\u00e4nligen bifoga betygsutdrag, kopia av examensarbete samt eventuella \u00f6vriga handlingar som du vill \u00e5beropa.\nOm anst\u00e4llningen \nAnst\u00e4llningen \u00e4r tidsbegr\u00e4nsad, enligt HF 5 kap \u00a7 7. Omfattningen \u00e4r heltid. Tilltr\u00e4de 1 januari 2027, eller enligt \u00f6verenskommelse. Placeringsort: Uppsala.\nUpplysningar om anst\u00e4llningen l\u00e4mnas av: Andreas Solders, 018-471 26 31, andreas.solders@physics.uu.se\nI denna rekrytering har vi ersatt det personliga brevet med fr\u00e5gor som du besvarar i samband med din ans\u00f6kan. Svaren kommer att anv\u00e4ndas som en del i urvalsprocessen.\nV\u00e4lkommen med din ans\u00f6kan senast den 30 september 2026, UFV-PA 2026\/2129\nObservera att detta \u00e4r en f\u00f6rkortad version av annonsen. F\u00f6r att se den fullst\u00e4ndiga annonsen v\u00e4nligen klicka p\u00e5 \u201dAns\u00f6k h\u00e4r\u201d eller se Uppsala universitets hemsida f\u00f6r jobbannonser","available_locales":["en"],"price":null,"currency":"SEK","status":"active","noindex":true,"location":{"address":"Box 256","full_address":null,"city":"Uppsala","country":"SE","latitude":59.8520701,"longitude":17.7188369},"metadata":{"region":"Uppsala l\u00e4n","duration":"6 m\u00e5nader eller l\u00e4ngre","employer":"UPPSALA UNIVERSITET","postcode":"75105","positions":1,"profession":"Doktorand","salary_type":"Fast m\u00e5nads- vecko- eller timl\u00f6n","employer_url":"http:\/\/www.uu.se\/jobb\/","scope_of_work":"100\u2013100 %","working_hours":"Heltid","employment_type":"full_time","occupation_field":"Pedagogik","employer_workplace":"Uppsala universitet, Institutionen f\u00f6r fysik och astronomi","employment_type_label":"Vanlig anst\u00e4llning"},"user_id":null,"is_sponsored":false,"views_count":28,"ai_views_count":12,"bot_views_count":0,"ai_vendor_counts":{"other":5,"openai":4,"anthropic":3},"visibility":"public","submission_source":null,"submission_ai_name":null,"has_owner_email":true,"can_contact_owner":true,"phone":"018-471 26 31","owner_email":"andreas.solders@physics.uu.se","images":[],"published_at":"2026-06-17T11:33:42+00:00","expires_at":"2026-09-30T23:59:59+00:00","created_at":"2026-06-21T04:38:03+00:00","updated_at":"2026-09-22T02:50:47+00:00"}}