{"data":{"id":213461,"slug":"postdoktor-inom-simuleringsbaserad-inferens-for-partikelfysik","type":"job","title":"Postdoctoral Researcher in Simulation-Based Inference for Particle Physics","description":"Do you want to work with machine learning and simulation-based inference in the search for dark matter (or other \"invisible\" signals of new physics) at the Large Hadron Collider, in an international environment with competent and friendly colleagues? We invite you to apply for a postdoctoral position at Uppsala University.\nThis is a shortened version of the job posting. The complete posting is available on Uppsala University's website, uu.se\/jobb.\nThe Department of Information Technology is Uppsala University's third largest department with approximately 350 employees. The position is located at the Division for Computational Science (TDB), one of the world's largest research environments in computational science with extensive activities in machine learning, optimization, and high-performance computing, and an important part of eSSENCE and SciLifeLab.\nThe hired researcher will join the Scientific Machine Learning research group at TDB and SciLifeLab (University Lecturer Prashant Singh), which develops methods and software for simulation-based inference, generative models, and robust machine learning, in close collaboration with the Theoretical Particle Physics group at the Department of Physics and Astronomy (Professor Stefano Moretti), which conducts research on beyond-standard-model phenomenology and dark matter and is a member of the CMS experiment at CERN. The project establishes a new cross-faculty collaboration where new simulation-based inference methodology is developed and applied directly to realistic analyses of collider data. The postdoctoral researcher will be jointly supervised by both groups and will receive support for conference travel as well as access to national HPC resources (NAISS) and local GPU infrastructure.\nThe position is part of the eSSENCE graduate school in data-intensive science, an arena where experts in computational science, data science, and data technology work closely with researchers in data-driven sciences, industry, and society. eSSENCE is a strategic research collaboration in e-science between Uppsala University, Lund University, and Ume\u00e5 University.\nProject Description\nThe search for dark matter at the LHC involves comparing high-dimensional collision data with detailed simulations whose likelihood cannot be calculated, only sampled. Simulation-based inference (SBI) addresses this by training neural networks, such as generative models based on flow matching, on simulated events. The project aims to develop efficient, robust, and calibrated SBI methods that account for event selection and systematic uncertainties, and to demonstrate them in realistic large-scale searches. The project is primarily based on simulated data, but there is also an opportunity to work with open data from ATLAS and\/or CMS. The methods are general and applicable far beyond particle physics.\nDuties\nResearch within the project, including method development, implementation, large-scale computational experiments, and publication, as well as presentations at international conferences, contributions to the group's open-source software, participation in eSSENCE graduate school activities, and involvement in student mentoring. A limited amount of teaching may be included (maximum 20%).\nQualification Requirements\nA PhD in machine learning, computational science, statistics, physics, or a related field, or a foreign degree assessed as equivalent to a PhD in these areas. The degree must be completed by the time the employment decision is made. Preferably, the degree should have been obtained no more than three years ago. When calculating the three-year period, the starting point is the application deadline. If there are special circumstances, such a degree may have been obtained earlier. Special circumstances include leave due to illness, parental leave, positions of trust within trade union organizations, etc.\nDocumented experience in machine learning, particularly deep generative models and\/or probabilistic modeling, as well as very strong programming skills in Python and a modern deep learning framework (e.g., PyTorch or JAX) are required. Strong knowledge of English in speaking and writing is required. The candidate must clearly demonstrate a high degree of self-motivation in the application. Great importance is placed on personal qualities such as creativity, attention to detail, a structured work approach, and the ability to work both independently and in an interdisciplinary team.\nDesirable\/Meriting Experience\nExperience with simulation-based inference, normalizing flows, flow matching, or diffusion models; with particle physics (e.g., MadGraph, Pythia, Delphes, or LHC data analysis); with large-scale training on GPU\/HPC systems, active learning, and open-source software development is meriting, as are publications at leading machine learning conferences or physics journals. Teaching experience (e.g., teaching, mentoring, supervision, or other pedagogical activities) is meriting but not required; particular emphasis is placed on activities that support student learning in data science, information technology, or related fields.\nApplication\nThe application should include:\nCV;\nCopy of relevant transcripts (in Swedish or English);\nList of publications; up to five selected publications in electronic format;\nA research description of previous and current research (max 1 page) and a proposal for future activities (max 1 page);\nContact information for two references.\nIn this recruitment, we have replaced the personal letter with questions that you answer in connection with your application. The answers are used as part of the selection process.\nAbout the Position\nThe position is time-limited for two years according to central collective agreement. Full-time. Start date November 1, 2026, or by agreement. Location: Uppsala.\nInformation about the position is provided by: University Lecturer Prashant Singh, prashant.singh@scilifelab.uu.se; Professor Stefano Moretti, stefano.moretti@physics.uu.se; Head of Division Elisabeth Larsson, elisabeth.larsson@it.uu.se.\nWelcome to submit your application by Thursday, October 15, 2026, UFV-PA 2026\/2734.","language":"sv","is_translated":true,"title_original":"Postdoktor inom simuleringsbaserad inferens f\u00f6r partikelfysik","description_original":"Vill du arbeta med maskininl\u00e4rning och simuleringsbaserad inferens f\u00f6r s\u00f6kandet efter m\u00f6rk materia (eller andra \"osynliga\" signaler av ny fysik) vid Large Hadron Collider, i en internationell milj\u00f6 med kompetenta och trevliga kollegor? V\u00e4lkommen att s\u00f6ka anst\u00e4llning som postdoktor p\u00e5 Uppsala universitet.\nDetta \u00e4r en f\u00f6rkortad version av annonsen. Den fullst\u00e4ndiga annonsen finns p\u00e5 Uppsala universitets webbplats, uu.se\/jobb.\nInstitutionen f\u00f6r informationsteknologi \u00e4r Uppsala universitets tredje st\u00f6rsta institution med drygt 350 anst\u00e4llda. Anst\u00e4llningen \u00e4r placerad vid avdelningen f\u00f6r ber\u00e4kningsvetenskap (TDB), en av v\u00e4rldens st\u00f6rsta forskningsmilj\u00f6er inom ber\u00e4kningsvetenskap med omfattande verksamhet inom bl.a. maskininl\u00e4rning, optimering och h\u00f6gprestandaber\u00e4kningar, och en viktig del av eSSENCE och SciLifeLab.\nDen som anst\u00e4lls ing\u00e5r i forskargruppen Scientific Machine Learning vid TDB och SciLifeLab (universitetslektor Prashant Singh), som utvecklar metoder och programvara f\u00f6r simuleringsbaserad inferens, generativa modeller och robust maskininl\u00e4rning, i n\u00e4ra samarbete med gruppen f\u00f6r teoretisk partikelfysik vid institutionen f\u00f6r fysik och astronomi (professor Stefano Moretti), som forskar om fenomenologi bortom standardmodellen och m\u00f6rk materia och \u00e4r medlem av CMS-experimentet vid CERN. Projektet etablerar ett nytt fakultets\u00f6verskridande samarbete d\u00e4r ny metodik f\u00f6r simuleringsbaserad inferens utvecklas och till\u00e4mpas direkt i realistiska analyser av kolliderardata. Postdoktorn handleds gemensamt av b\u00e5da grupperna och f\u00e5r st\u00f6d f\u00f6r konferensresor samt tillg\u00e5ng till nationella HPC-resurser (NAISS) och lokal GPU-infrastruktur.\nAnst\u00e4llningen \u00e4r en del av eSSENCE forskarskola i dataintensiv vetenskap, en arena d\u00e4r experter inom ber\u00e4kningsvetenskap, datavetenskap och datateknik arbetar n\u00e4ra forskare inom datadrivna vetenskaper, industri och samh\u00e4lle. eSSENCE \u00e4r ett strategiskt forskningssamarbete inom e-vetenskap mellan Uppsala universitet, Lunds universitet och Ume\u00e5 universitet.\nProjektbeskrivningS\u00f6kandet efter m\u00f6rk materia vid LHC inneb\u00e4r att h\u00f6gdimensionella kollisionsdata j\u00e4mf\u00f6rs med detaljerade simuleringar vars likelihood inte kan ber\u00e4knas, endast samplas. Simuleringsbaserad inferens (SBI) angriper detta genom att tr\u00e4na neurala n\u00e4tverk, s\u00e5som generativa modeller baserade p\u00e5 flow matching, p\u00e5 simulerade h\u00e4ndelser. Projektet syftar till att utveckla effektiva, robusta och kalibrerade SBI-metoder som tar h\u00e4nsyn till h\u00e4ndelseselektion och systematiska os\u00e4kerheter, och att demonstrera dem p\u00e5 realistiska s\u00f6kningar i stor skala. Projektet bygger i f\u00f6rsta hand p\u00e5 simulerade data, men det finns \u00e4ven m\u00f6jlighet att arbeta med \u00f6ppna data fr\u00e5n ATLAS och\/eller CMS. Metoderna \u00e4r generella och till\u00e4mpliga l\u00e5ngt utanf\u00f6r partikelfysiken.\nArbetsuppgifter Forskning inom projektet, inklusive metodutveckling, implementering, storskaliga ber\u00e4kningsexperiment och publicering, samt presentation vid internationella konferenser, bidrag till gruppens \u00f6ppna programvara, deltagande i eSSENCE forskarskolas aktiviteter och medverkan i handledning av studenter. En begr\u00e4nsad andel undervisning kan ing\u00e5 (h\u00f6gst 20 %).\nKvalifikationskrav Doktorsexamen i maskininl\u00e4rning, ber\u00e4kningsvetenskap, statistik, fysik eller ett n\u00e4rliggande omr\u00e5de, eller en utl\u00e4ndsk examen som bed\u00f6ms motsvara doktorsexamen inom dessa omr\u00e5den. Examen ska vara uppfyllt senast vid tidpunkten d\u00e5 anst\u00e4llningsbeslutet fattas. Fr\u00e4mst b\u00f6r den komma ifr\u00e5ga som har avlagt examen f\u00f6r h\u00f6gst tre \u00e5r sedan. Vid ber\u00e4kning av ramtiden om tre \u00e5r \u00e4r utg\u00e5ngspunkten sista ans\u00f6kningsdag. Om det finns s\u00e4rskilda sk\u00e4l kan s\u00e5dan examen ha avlagts tidigare. Med s\u00e4rskilda sk\u00e4l avses ledighet p\u00e5 grund av sjukdom, f\u00f6r\u00e4ldraledighet, f\u00f6rtroendeuppdrag inom fackliga organisationer, etc.\nDokumenterad erfarenhet av maskininl\u00e4rning, s\u00e4rskilt djupa generativa modeller och\/eller probabilistisk modellering, samt mycket goda programmeringskunskaper i Python och ett modernt ramverk f\u00f6r djupinl\u00e4rning (t.ex. PyTorch eller JAX) kr\u00e4vs. Goda kunskaper i engelska i tal och skrift kr\u00e4vs. Kandidaten ska tydligt dokumentera en h\u00f6g grad av sj\u00e4lvmotivation i ans\u00f6kan. Stor vikt l\u00e4ggs vid personliga egenskaper s\u00e5som kreativitet, noggrannhet, ett strukturerat arbetss\u00e4tt samt f\u00f6rm\u00e5ga att arbeta b\u00e5de sj\u00e4lvst\u00e4ndigt och i ett tv\u00e4rvetenskapligt team.\n\u00d6nskv\u00e4rt\/meriterande i \u00f6vrigt Erfarenhet av simuleringsbaserad inferens, normaliserande fl\u00f6den, flow matching eller diffusionsmodeller; av partikelfysik (t.ex. MadGraph, Pythia, Delphes eller analys av LHC-data); av storskalig tr\u00e4ning p\u00e5 GPU\/HPC-system, aktiv inl\u00e4rning och \u00f6ppen programvaruutveckling \u00e4r meriterande, liksom publikationer vid ledande maskininl\u00e4rningskonferenser eller fysiktidskrifter. Undervisningserfarenhet (t.ex. undervisning, handledning, mentorskap eller andra pedagogiska aktiviteter) \u00e4r meriterande men inte ett krav; s\u00e4rskild vikt l\u00e4ggs vid aktiviteter som st\u00f6djer studenters l\u00e4rande inom datavetenskap, informationsteknologi eller n\u00e4rliggande \u00e4mnen.\nAns\u00f6kan Ans\u00f6kan ska inneh\u00e5lla:\nCV;\nkopia av relevanta betygsdokument (p\u00e5 svenska eller engelska);\npublikationslista; upp till fem utvalda publikationer i elektroniskt format;\nen forskningsbeskrivning av tidigare och nuvarande forskning (max 1 sida) samt ett f\u00f6rslag till framtida aktiviteter (max 1 sida);\nkontaktuppgifter till tv\u00e5 referenser.\nI denna rekrytering har vi ersatt det personliga brevet med fr\u00e5gor som du besvarar i samband med din ans\u00f6kan. Svaren anv\u00e4nds som en del i urvalsprocessen.\n\nOm anst\u00e4llningen Anst\u00e4llningen \u00e4r tidsbegr\u00e4nsad i tv\u00e5 \u00e5r enligt centralt kollektivavtal. Omfattningen \u00e4r heltid. Tilltr\u00e4de 1 november 2026 eller enligt \u00f6verenskommelse. Placeringsort: Uppsala.\nUpplysningar om anst\u00e4llningen l\u00e4mnas av: Universitetslektor Prashant Singh, prashant.singh@scilifelab.uu.se; Professor Stefano Moretti, stefano.moretti@physics.uu.se; Avdelningsf\u00f6rest\u00e5ndare Elisabeth Larsson, elisabeth.larsson@it.uu.se.\nV\u00e4lkommen med din ans\u00f6kan senast torsdagen 15 oktober 2026, UFV-PA 2026\/2734.","available_locales":["en"],"price":null,"currency":"SEK","status":"active","noindex":true,"location":{"address":"box 256","full_address":null,"city":"Uppsala","country":"SE","latitude":59.8710738,"longitude":17.5946002},"metadata":{"region":"Uppsala l\u00e4n","duration":"6 m\u00e5nader eller l\u00e4ngre","employer":"Uppsala Universitet","postcode":"75200","positions":1,"profession":"Postdoktor\/Postdoc","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 informationsteknologi","experience_required":true,"employment_type_label":"Vanlig anst\u00e4llning"},"user_id":null,"is_sponsored":false,"views_count":0,"ai_views_count":3,"bot_views_count":5,"ai_vendor_counts":{"meta":1,"openai":1,"anthropic":1},"visibility":"public","submission_source":null,"submission_ai_name":null,"has_owner_email":true,"can_contact_owner":true,"phone":null,"owner_email":"prashant.singh@scilifelab.uu.se","images":[],"published_at":"2026-09-15T15:08:45+00:00","expires_at":"2026-10-15T23:59:59+00:00","created_at":"2026-09-16T03:22:28+00:00","updated_at":"2026-09-22T03:13:54+00:00"}}