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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

24.03.2020

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

  • 24.03.2020

  • 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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17.03.2020

EMPA

Postdoc position on “Self-centering and confining memory steel reinforcements in concrete bridge columns"

  • EMPA

  • 8600Dübendorf

  • 17.03.2020

  • Vollzeitstelle

Project description and your tasks:Strengthening of existing RC bridges becomes more and more important due to the large number of existing bridges, which are aging or were designed according to old standards. Considering seismic loading, strengthening is critical as most of the bridge columns do not satisfy the new regulations for seismic performance. Prestressing is known to provide recentering capabilities. However, the available techniques are either vulnerable to corrosion or are prohibitively expensive. In this project, an innovative application methodology for selfcentering and active confinement of concrete columns in bridges by using memory steel reinforcement will be developed. The proposed technology could have tremendous societal impact with respect to saving lives during and after extreme events and economic well-being of the affected area. By keeping bridges and the highway network open to traffic, ambulances, fire trucks, first responders, and damage assessors will continue to have the mobility that existed prior to the disaster. The main goals of the projects are to study and develop the details of such a system. Effectiveness and practicality of the methodology will be studied both by finite element modeling and large-scale experiments. A design method for civil engineers will be proposed and will be used by re-fer AG. Lastly, a pilot project will demonstrate and prove the feasibility of the proposed method on site. The project includes both experimental and modeling (analytical and finite element) tasks.
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17.03.2020

EMPA

PhD Student for “Computer aided multi-objective aerogel process optimization through model fitting”

  • EMPA

  • 8600Dübendorf

  • 17.03.2020

  • Vollzeitstelle

Description Aerogels are a class of highly porous nanomaterials with interesting properties for many applications. The technical relevance of aerogels as insulation materials has come to life with their second wave of industrialization around 2004. The focus of current research lies on life science and environmental applications, which are still further away from commercialization. The key properties of aerogels – such as thermal conductivity, mechanical stability, sorption – critically depend on their microstructure. Thus, optimization of aerogels towards a specific application generally involves optimization of their microstructure. However, mostly due to their nanoscale characteristic length scale, there are currently no methods to directly determine the true 3D microstructure of aerogels. State-of-the-art techniques such as electron microscopy, physisorption or small angle scattering (SAS), only provide partial structural information. However, the combination of the different available techniques should provide sufficient data to allow the determination of their 3D microstructure.Today the optimization of aerogels towards a specific application is generally done via experimental studies. However the amount of input data (i.e. experiments or trial runs) required for full optimization will in many cases exceed the experimentally feasible. This is a known problem in materials science leading to an increased popularity of modelling as a method to expand the input data, allowing the successful optimization of materials and microstructures for specific target applications. A reliable 3D microstructural aerogel model, developed by fitting simultaneously to data from different experimental techniques, will allow such an approach. The aim of this project is thus to develop such a 3D microstructural model and to show that the model will allow faster and more reliable optimization of aerogels towards a specific application. The goals of this project are:Implementation of different 3D aerogel structural models as well as the prediction of properties such as physisorption isotherms, small angle scattering and thermal conductivity.Determination of the 3D microstructure of different in-house synthesized aerogels based on fitting to multiple experimental results simultaneously (density, small angle scattering, physisorption isotherms, surface area).Comparison between calculated thermal conductivities based on the determined 3D microstructure and measured thermal conductivities. Study of computer generated 3D models to interpolate and extrapolate the experimental data set and determination of the optimal microstructure for minimal thermal conductivity.
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