Open PhD fellowships within NL (starting 1 November 2019, application deadline 24 July for bio and 29 August for physics)

Cellulose nanostructures as building block for functional materials (PhD program in Physics)

Nanosized structures obtained from cellulose are finding a large scientific and economical interest.  Their recover from food industry wastes using an eco-friendly approach will make nanocellulose a sustainable material with broad foreseen uses (from food chemistry to flame retardant and gas barrier film, to cite a few). The project is aimed at the set-up of non conventional procedures to extract the nanostructures and investigate the physical and chemical mechanisms to assemble them into composite functional materials.

Application should be submitted at https://www.unitn.it/en/ateneo/1940/announcement-of-selection

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Twin photons and single photon entanglement for QRNG and QKD (PhD program in Physics)

Based on an integrated silicon light source the PhD will elaborate two different devices which are instrumental for quantum information and quantum communication. These are quantum random number generators and quantum key distribution set-up. The activity will be carried on within the European project Qrange (https://qrange.eu/). The aim of this PhD is to develop and test a scheme of QRNG based on a silicon emitter. The second aim of the PhD is to develop and test a scheme of QKD based on single photon entanglement

Application should be submitted at https://www.unitn.it/en/ateneo/1940/announcement-of-selection

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Neuromorphic silicon photonics (PhD program in Physics)

Within the ERC project BACKUP (https://r1.unitn.it/back-up/), we aim investigate a photonic Extreme Learning Machine (PELM) which is an evolution of the so-called Reservoir Computing Network (RCN) paradigm. PELM is characterized by the easiness of training, which makes PELM quite feasible in silicon photonics. The aim of the PhD is to implement PELMs using Silicon photonics to understand the Si-PELM from the basic rules to the design of the optimal network and to boost its performances towards features extraction rather than simple classification problems.

Application should be submitted at https://www.unitn.it/en/ateneo/1940/announcement-of-selection

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Second Order Nonlinearities in p-i-n Silicon waveguides for entangled photon generation towards MIR quantum sensing – SiPDC (PhD program in Physics, Q@TN initiative)

In this project, we aim to demonstrate Spontaneous Parametric Down Conversion (SPDC) process in a silicon waveguide. An interesting way to do so relies on the application of strong DC fields across a Si waveguide by means of a series of lateral p-i-n junctions, which can enable processes like the Electric-Field-Induced Second Harmonic Generation (EFISH). The PhD student will take care of the measurements and will set-up quantum interferometry experiments to demonstrate the generation of entangled photons in silicon. Furthermore, the student will develop schemes for assessing the performance of the system and demonstrate a novel concept of sensing in the MIR by using entangled photons.

Application should be submitted at https://www.unitn.it/en/ateneo/1940/announcement-of-selection

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Photonics for brain circuit physiology (PhD program in biomolecular sciences)

Within the ERC project BACKUP, we aim at identifying the basic principles governing neural engram: a group of neurons that can be recruited together as a consequence of a learning process. We will develop an integrated photonic-neuromorphic-computing platform for coding, consolidation, storage and retrieval of a memory trace created in vitro. Molecular assessments of artificial neural engram will further provide relevant mechanistic information about the significance and mode of action of a memory process.

Application should be submitted at https://www.unitn.it/en/ateneo/1961/announcement-of-selection

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Fully-funded Postdoc position in “Biological neural-network signal encoding using Artificial Intelligence”

The goal of this project is to propose and develop a general methodology for biological neuronal network signal encoding. Specifically, we will use a Multi-Electrode Array (MEA) to access the activity of a culture of in-vitro alive neurons. The MEA response is a set of time-varying signals, one signal per electrode. Because of  the weak currents, the signals have a very low Signal-to-Noise-Ratio (SNR). These raw signals will be used to train an artificial neural network in order to predict (“encode”) the biological neuron activity. Particularly challenging is to automatically extract significant features of the biological neuronal network behaviour from the noisy signals. The project will investigate Deep Learning methods for brain (neuronal network) activity encoding and will generalize the proposed methodology to different kinds of brain activity signals (e.g., fMRI, MEG, etc.).

For more details: Postdoc position

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