Using pliable materials for the construction of robot bodies presents new and interesting challenges for the robotics community. Within the EU project entitled STIFFness controllable Flexible & Learnable manipulator for surgical Operations (STIFF-FLOP), a bendable, segmented robot arm has been developed. The exterior of the arm is composed of a soft material (silicone), encasing an internal structure that contains air-chamber actuators and a variety of sensors for monitoring applied force, position and shape of the arm as it bends. Due to the physical characteristics of the arm, a proper model of robot kinematics and dynamics is difficult to infer from the sensor data. Here we propose a non-linear approach to predicting the robot arm posture, by training a feed-forward neural network with a structured series of pressures values applied to the arm's actuators. The model is developed across a set of seven different experiments. Because the STIFF-FLOP arm is intended for use in surgical procedures, traditional methods for position estimation (based on visual information or electromagnetic tracking) will not be possible to implement. Thus the ability to estimate pose based on data from a custom fiber-optic bending sensor and accompanying model is a valuable contribution. Results are presented which demonstrate the utility of our non-linear modelling approach across a range of data collection procedures.