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EMPA

Jobs und Ausbildung

An unseren drei Standorten Dübendorf, St. Gallen und Thun beschäftigen wir rund 1000 Mitarbeitende aus mehr als 50 Ländern. Unsere Mitarbeitenden schätzen das dynamische, innovative Forschungsumfeld an der Empa mit multikultureller Atmosphäre und internationaler Ausstrahlung. Ausserdem profitieren sie von unserem weitverzweigten Netzwerk in Industrie und Forschung. Die Empa ist auch eine erstklassige Adresse, wenn es um Ausbildung geht; wir bilden jedes Jahr rund 200 Studierende und PraktikantInnen aus, dazu kommen mehr als 40 Lernende in verschiedensten Berufen sowie an die 200 Doktorierende. Die Empa – auf jeden Fall eine gute Wahl für Arbeit und Ausbildung.

EMPA

Überlandstrasse 129
8600Dübendorf

04.06.2020

EMPA

PhD Position on “Fracture failure of adhesively-bonded shape memory alloy (SMA) joints”

  • EMPA

  • 8600Dübendorf

  • 04.06.2020

  • Vollzeitstelle

Objectives of the research:A Fe-Mn-Si shape memory alloy (SMA) has been recently developed at Empa. The Empa iron-based SMA (Fe-SMA) exhibits a shape memory effect (SME) that can generate high recovery stresses. A stretched Fe-SMA member tries to recover its original shape upon heating followed by cooling to the ambient temperature (i.e., the so called 'activation process'). High tensile stresses are generated in the Fe-SMA member when its deformation is constrained during the activation process. A (recovery) tensile stress up to 360 MPa in the Fe-SMA is attainable by being heated to 160°C and cooled to the room temperature. This feature is very desirable for prestressing applications. Desirable compressive stresses are generated in the parent structure to which the prestressed Fe-SMA is attached. The combination of the excellent mechanical properties, lower cost of the Fe-SMA (compared to that of NiTi- and Cu-based SMAs), easier installation and prestressing process (in comparison to prestressed steel and CFRP members) makes this alloy particularly at-tractive to be applied as prestressing elements for strengthening aging metallic structures in different sections. This PhD project undertakes the research initiative to study the fracture failure of adhesively bonded Fe-SMA joints with the further objective of developing an adhesively bonded prestressed Fe-SMA solution for fatigue strengthening of vast aging metallic structures in different sectors (e.g., civil, mechanical and aerospace areas). Fundamentally, the integrity of the adhesively bonded Fe-SMA subjected to quasi-static loading conditions has to be guaranteed first, though the ultimate objective is to bond the Fe-SMA for fatigue strengthening. PhD Programm:You will be registered at the Institute of Structural Engineering (IBK) at ETH Zurich. The funding is assured for 4 years by the Swiss National Science Foundation (SNSF) awarded to Empa. The project is a collaborative work between the Sustainable Metallic Structures Group in the Structural Engineering Lab of Empa, the TU Delft (the Adhesion Institute at the Aerospace Structures & Materials) and the University of Salerno (Department of Civil Engineering). The working place and office will be at Empa, Dübendorf. During the course of the PhD study, you will spend 6-months in the Adhesion Institute at the TU Delft .
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02.06.2020

EMPA

an engineer or an intern (50%-100%) for: Data-driven and physics-based optimization of postharvest supply chains from farm to fork

  • EMPA

  • 8600Dübendorf

  • 02.06.2020

  • Teilzeitstelle 50-100%

Our laboratory. Empa’s Laboratory of Biomimetic Membranes and Textilesaims to develop materials and systems for the protection of the human body and its health. The products developed in collaboration with industry are used in the fields of occupational safety, sport, medical applications, and health-tech.Background.Optimizing postharvest cold chains of fruits and vegetables across all unit operations is of key importance to maintain fresh food quality and to reduce food losses. Temperature and the gas composition in the air are affecting decay and food quality, so they need to be controlled during precooling, refrigerated transport, and cold storage. By optimizing these environmental parameters, shelf life can be maximized. Currently, extensive monitoring is performed on the environmental conditions in food supply chains (air temperature and humidity). However, often, such sensing only covers a part of the cold chain and not the entire journey from farm to fork. Taking into account the entire journey is essential to quantify how the fresh-food quality evolves, and what the final quality and shelf life are that the retailers, and thus the consumer, receive. In addition, only simplified analyses are performed on these huge datasets. This makes that there is most probably a lot of unexplored information in the data. Objective. The key objective is to better use this sensor data to identify when and where the quality loss occurs, and how commercial cold chains can be improved, to reduce food loss.To this end, data-driven and physics-based modeling are used, among others by integration into digital twins.This project is performed in collaboration with a Swiss retailer. In this project, you will be able to improve future supply chains of fruits and vegetables in order to reduce the environmental impact of the food we consume.Your tasks:Organize experiments in commercial cold chains, process the measured sensor data statistically, reformat data in databases, and analyze data for variability.Analyze the data with statistical techniques (e.g. PCA, Monte Carlo) to identify critical problem locations in the cold chain. As a next step, more advanced data-driven techniques, such as machine learning, can be explored.Use this data to build up and use physics-based and/or data-driven digital twinsPropose solutions to improve the shelf life and reduce food losses and test these solutions in the field by full-scale trails.Support other physics-based and data-driven projects in the lab and set up a framework for quality assurance in modeling & simulationSupport in the organization of modeling and simulation events.The work will be performed at the facilities of Empa (St. Gallen). Regular visits to the facilities of the Swiss retailer company are likely to be required.
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02.06.2020

EMPA

PhD candidate in the field of Computational Fluid Dynamics (CFD) simulations

  • EMPA

  • 8600Dübendorf

  • 02.06.2020

  • Vollzeitstelle

The ReMoVeS project will employ roadside stationary remote sensing (RSD) measurements of NOx concentrations in the wake flow in the downstream of moving vehicles. These measurements are planned in different measurement sites in Switzerland. Based on the RSD measurements, exhaust gas concentrations in the wake of different vehicles will be compared, however, there do not exist any correlations with the emissions in the tailpipe of each individual vehicle. The new PhD tendered herewith aims in analyzing the distribution of a specie in the wake flow downstream of a vehicle. The simulations should enable the association of the specie concentration as measured by the RSD in the vehicle wake with the concentration in exhaust tailpipe. Numerical simulations of the air flow around the moving vehicle and the diffusion effects of pollutants in this environment will deliver substantial understanding of the dilution physics and the main impacting parameters and dependencies. These simulations should be tuned by a selective validation through experiments and measurements.The activities of the 3-years limited position includeCFD simulations of the wake flow in the downstream of a moving vehicleIdentification of correlations between the species concentrations in the vehicle wake flow and those in the tailpipeMatching of the simulation results with remote sensing and PEMS measurementsData evaluation and cooperation in scientific publications
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