Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back Sample Clauses

Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back. Research and Innovation Action SC1-PM-17-2017: Personalised computer models and in-silico systems for well-being Deliverable: D4.3b - Final Design and Implementation of the Preliminary In Silico Resilience Trajectory Predictor Due date of deliverable: (31-03-2022) Actual submission date: (28-04-2022) Start date of Project: 01 November 2017 Duration: 54 months (including a 6 month extension) Responsible WP4 The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777167 Dissemination level PU Public PP Restricted to other programme participants (including the Commission Service RE Restricted to a group specified by the consortium (including the Commission Services) X CO Confidential, only for members of the consortium (excluding the Commission Services) 0. Document Info‌
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Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back. Research and Innovation Action SC1-PM-17-2017: Personalised computer models and in-silico systems for well-being Deliverable: D4.4a Accuracy Evaluation, Finalization and Quality Assurance of the BOUNCE In Silico Resilience Trajectory Predictor (Stage 1) Due date of deliverable: (30-08-2021) Actual submission date: (10-09-2021)) Start date of Project: 01 November 2017 Duration: 54 months (extended duration) Responsible WP: ICCS The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777167 Dissemination level PU Public x PP Restricted to other programme participants (including the Commission Service RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (excluding the Commission Services) 0. Document Info‌
Sample 1
Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back. Research and Innovation Action SC1-PM-17-2017: Personalized computer models and in-silico systems for well-being Deliverable 8.4c: Data Management Plan Due date of deliverable: (30-04-2022) Actual submission date of the first version: (11-22-2019) Actual submission date of the second version: (30-04-2022) Start date of Project: 01 November 2017 Duration: 48 months Responsible WP: 8 The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 777167 Dissemination level PU Public x PP Restricted to other programme participants (including the Commission Service RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (excluding the Commission Services) 0. Document Info‌ 0.1. Author‌ Author Company E-mail Xxxxx Xxxxx CHAMP xxxxx.xxxxx@xxxxxxxxxxxxxxxxxxxx.xx Haridimos Kondylakis FORTH xxxxxxxx@xxx.xxxxx.xx Xxxxxxxx Xxxxxxxx FORTH xxxxxxxx@xxx.xxxxx.xx Xxxxxx Xxxxxx FORTH xxxxxxx@xxx.xxxxx.xx Xxxx Xxxxx FORTH xxxx.xxxxx@xxxxx.xxx Xxxxxxxxx Xxxxxxxxx FORTH xxxxxxxxx@xxx.xx Xxxxxx Xxxxxxx SiLo xxxxxxxx@xx.xxxxxxxxxxxxx.xx Xxxxxx Xxxxxx SiLo xxxxxxx@xxxxxxxxxxxxx.xx Xxxxxxxx-Xxxxxxx Xxxxx HUS xxxxx.xxxxxxxx-xxxxxxx@xxx.xx Xxxxxx Xxxxxxx CHAMP xxxxxx.xxxxxxx@xxxxxxxx.xxxxxxxxxxxxx.xxx Xxxxxxxx Xxxxxxxx IEO xxxxxxxx.xxxxxxxx@xxxxx.xx Xxxxxxxxx Xxxxxxxxxx XXX xxxxxxxxx.xxxxxxxxxx@xxx.xx
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Predicting Effective Adaptation to Breast Cancer to Help Women to BOUNCE Back. Research and Innovation Action SC1-PM-17-2017: Personalised computer models and in-silico systems for well-being
Sample 1

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