Postes disponibles au Département de Physique

  • Collaborateur technique/scientifique (H/F) à 80-100% - Technischen/Wissenschaftlichen Mitarbeiter (m/w) zu 80-100%.

    Domaine d’activités: Encadrement des travaux pratiques d’électronique; support informatique; support pour le laboratoire humide, y compris la maintenance de l'équipement pour la préparation et la caractérisation des échantillons.

    Exigences: Laborant en physique, physicien, Physico-chimiste (HES) ou formation équivalente; bon sens de la communication et esprit d’équipe; autonomie dans la réalisation de projets; maîtrise de la langue française ou allemande et bonnes connaissances de l’anglais souhaitées.

    Entrée en fonction: Au plus tôt le 01.04.2021

    Délai de postulation : 01.12.2020 ou jusqu’à la repourvue du poste

     

    Tätigkeitsgebiete: Betreuung von Elektronik-Praktika, Informatiksupport, Unterstützung des Nasslabors einschließlich der Wartung der Ausrüstung für die Probenvorbereitung und Charakterisierung.

    Anforderungen: Physiklaborant, Physiker, Physikochemiker (HTL) oder äquivalente Ausbildung; gute Kommunikationsfähigkeit und Team Geist ; Autonomie bei der Projektausführung ; Deutsch oder Französisch sowie gute Englischkentnisse.

    Eintrittsdatum: frühestens am 01.04.2021

    Bewerbungsfrist: 01.12.2020 oder bis die Stelle besetzt ist

     

    Renseignements - Auskunft :

    Prof. Dr. Guillermo Acuña

    Université de Fribourg

    Ch. du Musée 3

    1700 Fribourg

    Tel. 026 / 300 90 31

    http://physics.unifr.ch/

    guillermo.acuna@unifr.ch

  • PhD position - Online event selection using machine learning techniques with the ATLAS experiment - Workplace : CERN, with close collaboration with the Brazilian machine learning cluster in Rio de Janeiro.

    PhD position - University of Bern - Switzerland


    Online event selection using machine learning techniques with the ATLAS experiment
    The ATLAS experiment at CERN’s Large Hadron Collider is taking proton-proton collision data and is facing harsher and harsher conditions to keep up with its efficient operation under more stringent conditions, when LHC is being upgraded to HL-LHC, its high-luminosity version. At the collision point in the center of the ATLAS experiment, bunches of protons are colliding head on, leading to multiple proton-proton collisions being overlaid with every bunch crossing. With HL-LHC, 140 collisions, up from 40 collisions at present, will happen at every 25 ns interval, leading to pile-up of data in the ATLAS detector. Pile-up is making it harder to identify signatures that particles leave in the detector and thus particle identification will be harder to achieve. To keep online filtering within the targeted signal efficiency, processing latency and readout output rate, machine learning techniques may offer unprecedented solutions.


    Job description
    The successful candidate will become member of the ATLAS collaboration and will be involved in exploiting machine learning techniques for energy reconstruction and trigger selection based on calorimetry and tracking information. Using advanced computational intelligence models, a significant improvement of the online triggering efficiency is expected, which will be of high value in the search for rare events that enable new insight in the understanding of the Standard Model and in searches for new physics beyond the Standard Model.


    Requirements
    You are a highly skilled and enthusiastic researcher who enjoys working in the vibrant environment that large international collaborations offer. You should hold a master’s degree, have a strong interest in machine-learning techniques processing mass-amount of data in a time-critical environment, and be strongly interested in high energy physics experiments. You are a team-oriented and communicative person and act independently, show initiative and are driven to find solutions of which you will show that these perform within expectations. Good knowledge of English (written and oral) is mandatory. Your work place will be CERN and you will also be spending time at the Rio de Janeiro State University, who is a close partner in this project.


    Application:
    Candidates are requested to send a short letter of motivation, their CV, and names and contact information of references to the email address given below. The review of applications will continue until the position is filled.


    For further information, please contact:
    Prof. Dr. Hans Peter Beck

    hans.peter.beck@cern.ch